diff --git a/LICENSE b/LICENSE
index c31239079..c078d2f7e 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022-2023 Chan Zuckerberg Initiative
+Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/api/python/cellxgene_census/LICENSE b/api/python/cellxgene_census/LICENSE
index 9b6892998..c078d2f7e 100644
--- a/api/python/cellxgene_census/LICENSE
+++ b/api/python/cellxgene_census/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2022 Chan Zuckerberg Initiative
+Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/api/python/cellxgene_census/pyproject.toml b/api/python/cellxgene_census/pyproject.toml
index e601715ea..4e923b26c 100644
--- a/api/python/cellxgene_census/pyproject.toml
+++ b/api/python/cellxgene_census/pyproject.toml
@@ -7,7 +7,7 @@ name = "cellxgene_census"
 dynamic = ["version"]
 description = "API to facilitate the use of the CZ CELLxGENE Discover Census. For more information about the API and the project visit https://github.com/chanzuckerberg/cellxgene-census/"
 authors = [
-    { name = "Chan Zuckerberg Initiative", email = "soma@chanzuckerberg.com" }
+    { name = "Chan Zuckerberg Initiative Foundation", email = "soma@chanzuckerberg.com" }
 ]
 license = { text = "MIT" }
 readme = "README.md"
@@ -31,7 +31,7 @@ dependencies= [
     # NOTE: the tiledbsoma version must be >= to the version used in the Census builder, to
     # ensure that the assets are readable (tiledbsoma supports backward compatible reading).
     # Make sure this version does not fall behind the builder's tiledbsoma version.
-    "tiledbsoma~=1.4.3",
+    "tiledbsoma~=1.5.1",
     "anndata",
     "numpy>=1.21,<1.25",  # numpy is constrained by numba and the old pip solver
     "requests",
diff --git a/api/python/cellxgene_census/src/cellxgene_census/_experiment.py b/api/python/cellxgene_census/src/cellxgene_census/_experiment.py
index f8edb0ce2..68b846713 100644
--- a/api/python/cellxgene_census/src/cellxgene_census/_experiment.py
+++ b/api/python/cellxgene_census/src/cellxgene_census/_experiment.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023 Chan Zuckerberg Initiative
+# Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 #
 # Licensed under the MIT License.
 
diff --git a/api/python/cellxgene_census/src/cellxgene_census/_get_anndata.py b/api/python/cellxgene_census/src/cellxgene_census/_get_anndata.py
index 6751b31d1..22a6a284c 100644
--- a/api/python/cellxgene_census/src/cellxgene_census/_get_anndata.py
+++ b/api/python/cellxgene_census/src/cellxgene_census/_get_anndata.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023 Chan Zuckerberg Initiative
+# Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 #
 # Licensed under the MIT License.
 
diff --git a/api/python/cellxgene_census/src/cellxgene_census/_open.py b/api/python/cellxgene_census/src/cellxgene_census/_open.py
index fcc06fa55..8be55810c 100644
--- a/api/python/cellxgene_census/src/cellxgene_census/_open.py
+++ b/api/python/cellxgene_census/src/cellxgene_census/_open.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023 Chan Zuckerberg Initiative
+# Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 #
 # Licensed under the MIT License.
 
diff --git a/api/python/cellxgene_census/src/cellxgene_census/_presence_matrix.py b/api/python/cellxgene_census/src/cellxgene_census/_presence_matrix.py
index bd6f57e9f..aa8b6b183 100644
--- a/api/python/cellxgene_census/src/cellxgene_census/_presence_matrix.py
+++ b/api/python/cellxgene_census/src/cellxgene_census/_presence_matrix.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023 Chan Zuckerberg Initiative
+# Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 #
 # Licensed under the MIT License.
 
diff --git a/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py b/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py
index 7bbd3760c..f0ee91d4b 100644
--- a/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py
+++ b/api/python/cellxgene_census/src/cellxgene_census/_release_directory.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2023 Chan Zuckerberg Initiative
+# Copyright (c) 2022-2023 Chan Zuckerberg Initiative Foundation
 #
 # Licensed under the MIT License.
 
diff --git a/api/python/cellxgene_census/src/cellxgene_census/experimental/util/_csr_iter.py b/api/python/cellxgene_census/src/cellxgene_census/experimental/util/_csr_iter.py
index 4811b735c..96476ef95 100644
--- a/api/python/cellxgene_census/src/cellxgene_census/experimental/util/_csr_iter.py
+++ b/api/python/cellxgene_census/src/cellxgene_census/experimental/util/_csr_iter.py
@@ -22,6 +22,9 @@ def X_sparse_iter(
     reindex_sparse_axis: bool = True,
 ) -> Iterator[_RT]:
     """
+    NOTE: this API is deprecated as of version 1.8, and will be removed in version
+    1.9. Please use the `blockwise` iterator support introduced in tiledbsoma version 1.5.
+
     Iterate over rows (or columns) of the query results X matrix, with pagination to
     control peak memory usage for large result sets. Each iteration step yields:
         * obs_coords (coordinates)
@@ -78,7 +81,7 @@ def X_sparse_iter(
         ...             ...
 
     Lifecycle:
-        experimental
+        deprecated
 
     See also: https://github.com/single-cell-data/TileDB-SOMA/issues/1528
     """
diff --git a/api/python/cellxgene_census/tests/README.md b/api/python/cellxgene_census/tests/README.md
index 99dce0501..860f17e59 100644
--- a/api/python/cellxgene_census/tests/README.md
+++ b/api/python/cellxgene_census/tests/README.md
@@ -50,7 +50,953 @@ When run, please record the results in this file (below) and commit the change t
   - The Census version used for the test (i.e., the version aliased as `latest`). This can be easily captured using `cellxgene_census.get_census_version_description('latest')`
   - the cellxgene_census package version (ie., `cellxgene_census.__version__`)
 - any run notes
-- full output of: `pytest -v --durations=0 --expensive ./api/python/cellxgene_census/tests/`
+- full output of: `pytest -v --durations=0 --experimental --expensive ./api/python/cellxgene_census/tests/`
+
+### 2023-10-23
+
+- Host: EC2 instance type: `r6id.32xlarge`, all nvme mounted as swap.
+- Uname: Linux 6.2.0-1015-aws #15~22.04.1-Ubuntu SMP Fri Oct  6 21:37:24 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
+- Python & census versions:
+
+```python
+>>> import cellxgene_census, tiledbsoma
+>>> tiledbsoma.show_package_versions()
+tiledbsoma.__version__        1.5.1
+TileDB-Py tiledb.version()    (0, 23, 4)
+TileDB core version           2.17.4
+libtiledbsoma version()       libtiledb=2.17.4
+python version                3.10.12.final.0
+OS version                    Linux 6.2.0-1015-aws
+```
+
+**Pytest output:**
+
+```text
+============================= test session starts ==============================
+platform linux -- Python 3.10.12, pytest-7.4.3, pluggy-1.3.0 -- /usr/bin/python3
+cachedir: .pytest_cache
+rootdir: /home/ssm-user/cellxgene-census/api/python/cellxgene_census
+configfile: pyproject.toml
+plugins: requests-mock-1.11.0
+collecting ... collected 411 items
+
+api/python/cellxgene_census/tests/test_acceptance.py::test_load_axes[homo_sapiens] PASSED [  0%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_load_axes[mus_musculus] PASSED [  0%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_obs[2-None-homo_sapiens] PASSED [  0%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_obs[2-None-mus_musculus] PASSED [  0%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_obs[None-ctx_config1-homo_sapiens] PASSED [  1%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_obs[None-ctx_config1-mus_musculus] PASSED [  1%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_var[2-None-homo_sapiens] PASSED [  1%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_var[2-None-mus_musculus] PASSED [  1%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_var[None-ctx_config1-homo_sapiens] PASSED [  2%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_var[None-ctx_config1-mus_musculus] PASSED [  2%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[2-None-coords0-homo_sapiens] PASSED [  2%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[2-None-coords0-mus_musculus] PASSED [  2%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[None-ctx_config1-coords1-homo_sapiens] PASSED [  3%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[None-ctx_config1-coords1-mus_musculus] PASSED [  3%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[None-ctx_config2-coords2-homo_sapiens] PASSED [  3%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[None-ctx_config2-coords2-mus_musculus] PASSED [  3%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[None-ctx_config3-coords3-homo_sapiens] PASSED [  4%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_read_X[None-ctx_config3-coords3-mus_musculus] PASSED [  4%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[2-tissue=='aorta'-homo_sapiens] PASSED [  4%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[2-tissue=='aorta'-mus_musculus] PASSED [  4%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[2-tissue=='brain'-homo_sapiens] PASSED [  5%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[2-tissue=='brain'-mus_musculus] PASSED [  5%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[None-tissue=='aorta'-homo_sapiens] PASSED [  5%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[None-tissue=='aorta'-mus_musculus] PASSED [  5%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[None-tissue=='brain'-homo_sapiens] PASSED [  6%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_incremental_query[None-tissue=='brain'-mus_musculus] PASSED [  6%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[tissue=='aorta'-None-ctx_config0-homo_sapiens] PASSED [  6%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[tissue=='aorta'-None-ctx_config0-mus_musculus] PASSED [  6%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 10K cells-homo_sapiens] PASSED [  7%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 10K cells-mus_musculus] PASSED [  7%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 100K cells-homo_sapiens] PASSED [  7%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 100K cells-mus_musculus] PASSED [  7%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 250K cells-homo_sapiens] PASSED [  8%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 250K cells-mus_musculus] PASSED [  8%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 500K cells-homo_sapiens] PASSED [  8%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 500K cells-mus_musculus] PASSED [  8%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 750K cells-homo_sapiens] PASSED [  9%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 750K cells-mus_musculus] PASSED [  9%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 1M cells-homo_sapiens] PASSED [  9%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[First 1M cells-mus_musculus] PASSED [  9%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[tissue_general=='brain'-obs_coords7-ctx_config7-homo_sapiens] PASSED [  9%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[tissue_general=='brain'-obs_coords7-ctx_config7-mus_musculus] PASSED [ 10%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[cell_type=='neuron'-obs_coords8-ctx_config8-homo_sapiens] PASSED [ 10%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[cell_type=='neuron'-obs_coords8-ctx_config8-mus_musculus] PASSED [ 10%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[is_primary_data==True-obs_coords9-ctx_config9-homo_sapiens] PASSED [ 10%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[is_primary_data==True-obs_coords9-ctx_config9-mus_musculus] PASSED [ 11%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[None-obs_coords10-ctx_config10-homo_sapiens] PASSED [ 11%]
+api/python/cellxgene_census/tests/test_acceptance.py::test_get_anndata[None-obs_coords10-ctx_config10-mus_musculus] PASSED [ 11%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_version_directory PASSED [ 11%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_version_directory__lts_only PASSED [ 12%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_version_directory__exclude_lts PASSED [ 12%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_version_directory__include_retracted PASSED [ 12%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_version_directory__retraction_info PASSED [ 12%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_version_description_errors PASSED [ 13%]
+api/python/cellxgene_census/tests/test_directory.py::test_get_census_mirrors_directory PASSED [ 13%]
+api/python/cellxgene_census/tests/test_directory.py::test_live_directory_contents PASSED [ 13%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_value_filter PASSED [ 13%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_coords PASSED [ 14%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_allows_missing_obs_or_var_filter PASSED [ 14%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_x_layer[raw] PASSED [ 14%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_x_layer[normalized] PASSED [ 14%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_two_layers[layers0] PASSED [ 15%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_two_layers[layers1] PASSED [ 15%]
+api/python/cellxgene_census/tests/test_get_anndata.py::test_get_anndata_wrong_layer_names PASSED [ 15%]
+api/python/cellxgene_census/tests/test_get_helpers.py::test_get_experiment PASSED [ 15%]
+api/python/cellxgene_census/tests/test_get_helpers.py::test_get_presence_matrix[homo_sapiens] PASSED [ 16%]
+api/python/cellxgene_census/tests/test_get_helpers.py::test_get_presence_matrix[mus_musculus] PASSED [ 16%]
+api/python/cellxgene_census/tests/test_lts_compat.py::test_open[stable] PASSED [ 16%]
+api/python/cellxgene_census/tests/test_lts_compat.py::test_read_dataframe[stable] PASSED [ 16%]
+api/python/cellxgene_census/tests/test_lts_compat.py::test_read_arrays[stable] PASSED [ 17%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_stable PASSED [ 17%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_latest PASSED [ 17%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_with_context FAILED [ 17%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_invalid_args PASSED [ 18%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_errors PASSED [ 18%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_uses_correct_mirror PASSED [ 18%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_rejects_non_s3_mirror PASSED [ 18%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_works_if_no_relative_uri_specified PASSED [ 18%]
+api/python/cellxgene_census/tests/test_open.py::test_open_soma_defaults_to_stable PASSED [ 19%]
+api/python/cellxgene_census/tests/test_open.py::test_get_source_h5ad_uri PASSED [ 19%]
+api/python/cellxgene_census/tests/test_open.py::test_get_source_h5ad_uri_errors PASSED [ 19%]
+api/python/cellxgene_census/tests/test_open.py::test_download_source_h5ad PASSED [ 19%]
+api/python/cellxgene_census/tests/test_open.py::test_download_source_h5ad_errors PASSED [ 20%]
+api/python/cellxgene_census/tests/test_open.py::test_opening_census_without_anon_access_fails_with_bogus_creds PASSED [ 20%]
+api/python/cellxgene_census/tests/test_open.py::test_can_open_with_anonymous_access PASSED [ 20%]
+api/python/cellxgene_census/tests/test_util.py::test_uri_join PASSED     [ 20%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_non_batched[6-3-pytorch_x_value_gen-True] PASSED [ 21%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_non_batched[6-3-pytorch_x_value_gen-False] PASSED [ 21%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__all_batches_full_size[6-3-pytorch_x_value_gen-True] PASSED [ 21%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__all_batches_full_size[6-3-pytorch_x_value_gen-False] PASSED [ 21%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_unique_soma_joinids[obs_range0-3-pytorch_x_value_gen-True] PASSED [ 22%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_unique_soma_joinids[obs_range1-3-pytorch_x_value_gen-False] PASSED [ 22%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__partial_final_batch_size[5-3-pytorch_x_value_gen-True] PASSED [ 22%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__partial_final_batch_size[5-3-pytorch_x_value_gen-False] PASSED [ 22%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__exactly_one_batch[3-3-pytorch_x_value_gen-True] PASSED [ 23%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__exactly_one_batch[3-3-pytorch_x_value_gen-False] PASSED [ 23%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__empty_query_result[6-3-pytorch_x_value_gen-True] PASSED [ 23%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__empty_query_result[6-3-pytorch_x_value_gen-False] PASSED [ 23%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_sparse_output__non_batched[6-3-pytorch_x_value_gen-True] PASSED [ 24%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_sparse_output__non_batched[6-3-pytorch_x_value_gen-False] PASSED [ 24%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_sparse_output__batched[6-3-pytorch_x_value_gen-True] PASSED [ 24%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_sparse_output__batched[6-3-pytorch_x_value_gen-False] PASSED [ 24%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__partial_soma_batches_are_concatenated[10-1-pytorch_x_value_gen-True] PASSED [ 25%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_batching__partial_soma_batches_are_concatenated[10-1-pytorch_x_value_gen-False] PASSED [ 25%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_encoders[3-3-pytorch_x_value_gen] PASSED [ 25%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_multiprocessing__returns_full_result[6-3-pytorch_x_value_gen] PASSED [ 25%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_distributed__returns_data_partition_for_rank[6-3-pytorch_x_value_gen] PASSED [ 26%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_distributed_and_multiprocessing__returns_data_partition_for_rank[12-3-pytorch_x_value_gen] PASSED [ 26%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__non_batched[3-3-pytorch_x_value_gen-True] PASSED [ 26%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__non_batched[3-3-pytorch_x_value_gen-False] PASSED [ 26%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__batched[6-3-pytorch_x_value_gen-True] PASSED [ 27%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__batched[6-3-pytorch_x_value_gen-False] PASSED [ 27%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test__X_tensor_dtype_matches_X_matrix[6-3-pytorch_x_value_gen-True] PASSED [ 27%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test__X_tensor_dtype_matches_X_matrix[6-3-pytorch_x_value_gen-False] PASSED [ 27%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test__pytorch_splitting[10-1-pytorch_x_value_gen] PASSED [ 27%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test__shuffle[16-1-pytorch_seq_x_value_gen] PASSED [ 28%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__multiprocess_sparse_matrix__fails SKIPPED [ 28%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__multiprocess_dense_matrix__ok SKIPPED [ 28%]
+api/python/cellxgene_census/tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__unsupported_params__fails PASSED [ 28%]
+api/python/cellxgene_census/tests/experimental/ml/huggingface/test_geneformer.py::test_GeneformerTokenizer[4] SKIPPED [ 29%]
+api/python/cellxgene_census/tests/experimental/ml/huggingface/test_geneformer.py::test_GeneformerTokenizer[100000] SKIPPED [ 29%]
+api/python/cellxgene_census/tests/experimental/ml/huggingface/test_geneformer.py::test_GeneformerTokenizer_docstring_example SKIPPED [ 29%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 29%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 30%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 30%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 30%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 30%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 31%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 31%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 31%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 31%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 32%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 32%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 32%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 32%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 33%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 33%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 33%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 33%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 34%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 34%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 34%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 34%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 35%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 35%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 35%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 35%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 36%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 36%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 36%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 36%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 36%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 37%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 37%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 37%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 37%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 38%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 38%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 38%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 38%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 39%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 39%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 39%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 39%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 40%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 40%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 40%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 40%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 41%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 41%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 41%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 41%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 42%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 42%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 42%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 42%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 43%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 43%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 43%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 43%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 44%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 44%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 44%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 44%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 45%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 45%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 45%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 45%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 45%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 46%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 46%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 46%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 46%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 47%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 47%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 47%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 47%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 48%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 48%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 48%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 48%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 49%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 49%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 49%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 49%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 50%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 50%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 50%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 50%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 51%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 51%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 51%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 51%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 52%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 52%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 52%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 52%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 53%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 53%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 53%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 53%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 54%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 54%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 54%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 54%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 54%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 55%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 55%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 55%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 55%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 56%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 56%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 56%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 56%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 57%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 57%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 57%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 57%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 58%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 58%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 58%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 58%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 59%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 59%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 59%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 59%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"] PASSED [ 60%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"] PASSED [ 60%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]] PASSED [ 60%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"] PASSED [ 60%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-tissue_general == "liver" and is_primary_data == True-None-None] PASSED [ 61%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-is_primary_data == True and tissue_general == "heart"-dataset_id-None] PASSED [ 61%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-is_primary_data == True and tissue_general == "heart"-batch_key2-None] PASSED [ 61%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-is_primary_data == True-dataset_id-obs_coords3] PASSED [ 61%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[homo_sapiens-Homo sapiens-is_primary_data == True-dataset_id-obs_coords4] FAILED [ 62%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_error_cases PASSED [ 62%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_max_loess_jitter_error PASSED [ 62%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_user_defined_batch_key_func[None] PASSED [ 62%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_user_defined_batch_key_func[suspension_type] PASSED [ 63%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_user_defined_batch_key_func[batch_key2] PASSED [ 63%]
+api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_hvg_user_defined_batch_key_func[batch_key3] PASSED [ 63%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-1-101] PASSED [ 63%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-1-53] PASSED [ 63%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-3-101] PASSED [ 64%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-3-53] PASSED [ 64%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-11-101] PASSED [ 64%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-11-53] PASSED [ 64%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-101-101] PASSED [ 65%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-101-53] PASSED [ 65%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-1-101] PASSED [ 65%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-1-53] PASSED [ 65%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-3-101] PASSED [ 66%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-3-53] PASSED [ 66%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-11-101] PASSED [ 66%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-11-53] PASSED [ 66%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-101-101] PASSED [ 67%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-101-53] PASSED [ 67%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-1-101] PASSED [ 67%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-1-53] PASSED [ 67%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-3-101] PASSED [ 68%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-3-53] PASSED [ 68%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-11-101] PASSED [ 68%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-11-53] PASSED [ 68%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-101-101] PASSED [ 69%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-101-53] PASSED [ 69%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-1-101] PASSED [ 69%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-1-53] PASSED [ 69%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-3-101] PASSED [ 70%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-3-53] PASSED [ 70%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-11-101] PASSED [ 70%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-11-53] PASSED [ 70%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-101-101] PASSED [ 71%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-101-53] PASSED [ 71%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-1-101] PASSED [ 71%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-1-53] PASSED [ 71%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-3-101] PASSED [ 72%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-3-53] PASSED [ 72%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-11-101] PASSED [ 72%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-11-53] PASSED [ 72%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-101-101] PASSED [ 72%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-101-53] PASSED [ 73%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-1-101] PASSED [ 73%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-1-53] PASSED [ 73%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-3-101] PASSED [ 73%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-3-53] PASSED [ 74%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-11-101] PASSED [ 74%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-11-53] PASSED [ 74%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-101-101] PASSED [ 74%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-101-53] PASSED [ 75%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-1-101] PASSED [ 75%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-1-53] PASSED [ 75%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-3-101] PASSED [ 75%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-3-53] PASSED [ 76%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-11-101] PASSED [ 76%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-11-53] PASSED [ 76%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-101-101] PASSED [ 76%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-101-53] PASSED [ 77%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-1-101] PASSED [ 77%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-1-53] PASSED [ 77%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-3-101] PASSED [ 77%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-3-53] PASSED [ 78%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-11-101] PASSED [ 78%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-11-53] PASSED [ 78%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-101-101] PASSED [ 78%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-101-53] PASSED [ 79%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-1-101] PASSED [ 79%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-1-53] PASSED [ 79%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-3-101] PASSED [ 79%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-3-53] PASSED [ 80%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-11-101] PASSED [ 80%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-11-53] PASSED [ 80%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-101-101] PASSED [ 80%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-101-53] PASSED [ 81%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-1-101] PASSED [ 81%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-1-53] PASSED [ 81%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-3-101] PASSED [ 81%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-3-53] PASSED [ 81%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-11-101] PASSED [ 82%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-11-53] PASSED [ 82%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-101-101] PASSED [ 82%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-101-53] PASSED [ 82%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-1-101] PASSED [ 83%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-1-53] PASSED [ 83%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-3-101] PASSED [ 83%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-3-53] PASSED [ 83%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-11-101] PASSED [ 84%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-11-53] PASSED [ 84%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-101-101] PASSED [ 84%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-101-53] PASSED [ 84%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-1-101] PASSED [ 85%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-1-53] PASSED [ 85%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-3-101] PASSED [ 85%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-3-53] PASSED [ 85%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-11-101] PASSED [ 86%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-11-53] PASSED [ 86%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-101-101] PASSED [ 86%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-101-53] PASSED [ 86%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar_nnz_only_batches_fails[1200-511] PASSED [ 87%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_meanvar_nnz_only_batches_fails[100001-57] PASSED [ 87%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_mean[1200-511-101] PASSED [ 87%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_mean[1200-511-53] PASSED [ 87%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_mean[100001-57-101] PASSED [ 88%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_mean[100001-57-53] PASSED [ 88%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-1-101] PASSED [ 88%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-1-53] PASSED [ 88%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-3-101] PASSED [ 89%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-3-53] PASSED [ 89%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-11-101] PASSED [ 89%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-11-53] PASSED [ 89%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-101-101] PASSED [ 90%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[1200-511-101-53] PASSED [ 90%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-1-101] PASSED [ 90%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-1-53] PASSED [ 90%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-3-101] PASSED [ 90%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-3-53] PASSED [ 91%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-11-101] PASSED [ 91%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-11-53] PASSED [ 91%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-101-101] PASSED [ 91%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_counts[100001-57-101-53] PASSED [ 92%]
+api/python/cellxgene_census/tests/experimental/pp/test_online.py::test_mean_fails_no_variables_or_samples PASSED [ 92%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-True-True-0] PASSED [ 92%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-True-True-1] PASSED [ 92%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-True-False-0] PASSED [ 93%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-True-False-1] PASSED [ 93%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-False-True-0] PASSED [ 93%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-False-True-1] PASSED [ 93%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-True-True-0] PASSED [ 94%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-True-True-1] PASSED [ 94%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-True-False-0] PASSED [ 94%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-True-False-1] PASSED [ 94%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-False-True-0] PASSED [ 95%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-False-True-1] PASSED [ 95%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-True-0] PASSED [ 95%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-True-1] PASSED [ 95%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-False-0] PASSED [ 96%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-False-1] PASSED [ 96%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-False-True-0] PASSED [ 96%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-False-True-1] PASSED [ 96%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-True-0] FAILED [ 97%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-True-1] FAILED [ 97%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-False-0] FAILED [ 97%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-False-1] FAILED [ 97%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-False-True-0] FAILED [ 98%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-False-True-1] FAILED [ 98%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance_nnz_only[mus_musculus-obs_coords0-True-True-0] PASSED [ 98%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance_nnz_only[mus_musculus-obs_coords0-True-True-1] PASSED [ 98%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance_no_flags PASSED [ 99%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance_empty_query[mus_musculus] PASSED [ 99%]
+api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance_wrong_axis PASSED [ 99%]
+api/python/cellxgene_census/tests/experimental/util/test_csr_iter.py::test_X_sparse_iter PASSED [ 99%]
+api/python/cellxgene_census/tests/experimental/util/test_csr_iter.py::test_X_sparse_iter_unsupported PASSED [100%]
+
+============================== slowest durations ===============================
+336.88s call     tests/test_acceptance.py::test_get_anndata[None-obs_coords10-ctx_config10-homo_sapiens]
+316.66s call     tests/test_acceptance.py::test_get_anndata[tissue_general=='brain'-obs_coords7-ctx_config7-homo_sapiens]
+276.94s call     tests/test_acceptance.py::test_get_anndata[None-obs_coords10-ctx_config10-mus_musculus]
+235.17s call     tests/test_acceptance.py::test_get_anndata[tissue_general=='brain'-obs_coords7-ctx_config7-mus_musculus]
+235.12s call     tests/test_acceptance.py::test_get_anndata[is_primary_data==True-obs_coords9-ctx_config9-mus_musculus]
+162.02s call     tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-is_primary_data == True-dataset_id-obs_coords3]
+151.34s call     tests/test_acceptance.py::test_get_anndata[First 1M cells-homo_sapiens]
+120.43s call     tests/test_acceptance.py::test_get_anndata[First 750K cells-homo_sapiens]
+114.92s call     tests/test_acceptance.py::test_get_anndata[First 1M cells-mus_musculus]
+83.01s call     tests/test_acceptance.py::test_incremental_query[None-tissue=='brain'-homo_sapiens]
+82.79s call     tests/test_acceptance.py::test_get_anndata[First 750K cells-mus_musculus]
+82.26s call     tests/test_acceptance.py::test_incremental_read_X[None-ctx_config1-coords1-homo_sapiens]
+76.06s call     tests/test_acceptance.py::test_get_anndata[First 500K cells-homo_sapiens]
+73.13s call     tests/test_acceptance.py::test_incremental_read_X[None-ctx_config2-coords2-homo_sapiens]
+71.89s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+63.83s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+52.57s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+51.95s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+51.87s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+51.51s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+51.41s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+51.37s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+51.14s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.82s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.71s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.48s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.41s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.28s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+50.21s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.14s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+50.05s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.70s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.68s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.62s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.59s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.58s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.51s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.25s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.20s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.20s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+49.19s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+48.98s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+48.91s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+48.80s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+48.70s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+48.64s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+48.60s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+48.59s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+48.43s call     tests/test_acceptance.py::test_incremental_read_X[None-ctx_config1-coords1-mus_musculus]
+48.31s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+48.15s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+48.10s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+47.99s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.92s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+47.81s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+47.75s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.70s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.65s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and assay == "Smart-seq2"]
+47.56s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.50s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.37s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.35s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.15s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+47.10s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+46.89s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+46.85s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+46.62s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+46.32s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+45.57s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+45.47s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+45.37s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+45.36s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+45.02s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+44.96s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+44.81s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+44.50s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+44.27s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+43.98s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+43.54s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general in ["heart", "lung"]]
+43.32s call     tests/test_acceptance.py::test_get_anndata[First 500K cells-mus_musculus]
+41.88s call     tests/test_acceptance.py::test_get_anndata[First 250K cells-homo_sapiens]
+40.84s call     tests/test_acceptance.py::test_incremental_query[2-tissue=='brain'-homo_sapiens]
+40.68s call     tests/test_acceptance.py::test_incremental_read_X[None-ctx_config2-coords2-mus_musculus]
+33.42s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-True-0]
+31.24s call     tests/test_acceptance.py::test_incremental_query[2-tissue=='brain'-mus_musculus]
+26.90s call     tests/test_acceptance.py::test_get_anndata[is_primary_data==True-obs_coords9-ctx_config9-homo_sapiens]
+26.41s call     tests/test_acceptance.py::test_incremental_query[None-tissue=='brain'-mus_musculus]
+23.43s call     tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-tissue_general == "liver" and is_primary_data == True-None-None]
+23.29s call     tests/experimental/pp/test_hvg.py::test_hvg_user_defined_batch_key_func[None]
+22.18s call     tests/test_acceptance.py::test_get_anndata[First 100K cells-homo_sapiens]
+21.76s call     tests/experimental/util/test_csr_iter.py::test_X_sparse_iter
+21.70s call     tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-is_primary_data == True and tissue_general == "heart"-dataset_id-None]
+19.91s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+19.86s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+19.11s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-True-1]
+18.78s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+18.71s call     tests/test_acceptance.py::test_get_anndata[First 250K cells-mus_musculus]
+18.26s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.95s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.91s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.82s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.68s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.68s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.64s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.56s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.54s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.47s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.46s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.43s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.42s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.41s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-False-1]
+17.41s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.40s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.39s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.36s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.31s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.28s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.17s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.17s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.16s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.13s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.11s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-False-True-1]
+17.06s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.06s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.04s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.03s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+17.01s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+17.00s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True-obs_coords2-True-False-0]
+17.00s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.98s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.93s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.91s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.89s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.89s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.85s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.70s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.69s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.67s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.62s call     tests/test_get_anndata.py::test_get_anndata_two_layers[layers0]
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+16.61s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.60s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key2-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.58s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.56s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.54s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+16.52s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.51s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.49s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.37s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-None-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.33s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-dataset_id-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+16.33s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
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+16.06s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+15.99s call     tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[mus_musculus-Mus musculus-is_primary_data == True and tissue_general == "heart"-batch_key2-None]
+15.97s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-batch_key3-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
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+15.60s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+15.52s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-None-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+15.51s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+15.49s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-True-True-0]
+15.40s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[stable-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+15.31s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+15.13s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-500-mus_musculus-is_primary_data == True and tissue_general == "liver"]
+14.94s call     tests/experimental/pp/test_hvg.py::test_hvg_vs_scanpy[latest-0.5-None-50-mus_musculus-is_primary_data == True and tissue_general == "skin of body"]
+14.55s call     tests/test_get_anndata.py::test_get_anndata_two_layers[layers1]
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+13.30s call     tests/test_get_anndata.py::test_get_anndata_allows_missing_obs_or_var_filter
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+13.22s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-True-False-1]
+13.20s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-False-True-1]
+12.98s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-is_primary_data == True and tissue_general == "heart"-obs_coords1-False-True-0]
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+10.44s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-True-False-0]
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+10.02s call     tests/experimental/pp/test_stats.py::test_mean_variance[mus_musculus-tissue_general == "liver" and is_primary_data == True-obs_coords0-False-True-0]
+9.61s call     tests/test_acceptance.py::test_incremental_query[2-tissue=='aorta'-homo_sapiens]
+9.19s call     tests/test_acceptance.py::test_incremental_query[None-tissue=='aorta'-mus_musculus]
+8.98s call     tests/test_acceptance.py::test_get_anndata[cell_type=='neuron'-obs_coords8-ctx_config8-mus_musculus]
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+5.20s call     tests/test_acceptance.py::test_get_anndata[First 10K cells-mus_musculus]
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+4.55s call     tests/test_open.py::test_get_source_h5ad_uri
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+2.88s call     tests/test_get_anndata.py::test_get_anndata_coords
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+2.04s call     tests/experimental/ml/test_pytorch.py::test_batching__partial_soma_batches_are_concatenated[10-1-pytorch_x_value_gen-True]
+1.90s call     tests/test_acceptance.py::test_incremental_read_obs[2-None-homo_sapiens]
+1.88s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-1-101]
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+1.80s call     tests/test_directory.py::test_get_census_version_directory__include_retracted
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+1.61s call     tests/test_open.py::test_download_source_h5ad
+1.51s call     tests/test_acceptance.py::test_incremental_read_var[None-ctx_config1-homo_sapiens]
+1.51s call     tests/test_acceptance.py::test_incremental_read_var[None-ctx_config1-mus_musculus]
+1.42s call     tests/test_acceptance.py::test_incremental_read_var[2-None-mus_musculus]
+1.40s call     tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-True-1]
+1.40s call     tests/experimental/ml/test_pytorch.py::test_batching__partial_final_batch_size[5-3-pytorch_x_value_gen-False]
+1.39s call     tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-False-0]
+1.33s call     tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-False-True-1]
+1.32s call     tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-False-1]
+1.29s call     tests/experimental/pp/test_stats.py::test_mean_variance_empty_query[mus_musculus]
+1.27s call     tests/experimental/ml/test_pytorch.py::test_batching__partial_final_batch_size[5-3-pytorch_x_value_gen-True]
+1.21s call     tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-False-True-0]
+1.16s setup    tests/test_open.py::test_download_source_h5ad
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+1.07s setup    tests/test_open.py::test_download_source_h5ad_errors
+1.06s call     tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__non_batched[3-3-pytorch_x_value_gen-False]
+1.05s call     tests/experimental/ml/test_pytorch.py::test_batching__all_batches_full_size[6-3-pytorch_x_value_gen-False]
+1.04s call     tests/experimental/ml/test_pytorch.py::test_unique_soma_joinids[obs_range1-3-pytorch_x_value_gen-False]
+0.98s call     tests/test_open.py::test_get_source_h5ad_uri_errors
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+0.95s call     tests/experimental/ml/test_pytorch.py::test_distributed__returns_data_partition_for_rank[6-3-pytorch_x_value_gen]
+0.94s call     tests/experimental/ml/test_pytorch.py::test__pytorch_splitting[10-1-pytorch_x_value_gen]
+0.93s call     tests/experimental/ml/test_pytorch.py::test_batching__all_batches_full_size[6-3-pytorch_x_value_gen-True]
+0.93s call     tests/experimental/ml/test_pytorch.py::test_distributed_and_multiprocessing__returns_data_partition_for_rank[12-3-pytorch_x_value_gen]
+0.93s call     tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__batched[6-3-pytorch_x_value_gen-True]
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+0.92s call     tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__non_batched[3-3-pytorch_x_value_gen-True]
+0.90s call     tests/experimental/ml/test_pytorch.py::test__shuffle[16-1-pytorch_seq_x_value_gen]
+0.87s call     tests/experimental/util/test_csr_iter.py::test_X_sparse_iter_unsupported
+0.77s call     tests/experimental/pp/test_hvg.py::test_hvg_error_cases
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+0.71s call     tests/experimental/ml/test_pytorch.py::test_sparse_output__non_batched[6-3-pytorch_x_value_gen-False]
+0.71s call     tests/experimental/ml/test_pytorch.py::test_non_batched[6-3-pytorch_x_value_gen-False]
+0.68s call     tests/experimental/ml/test_pytorch.py::test__X_tensor_dtype_matches_X_matrix[6-3-pytorch_x_value_gen-False]
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+0.58s call     tests/experimental/ml/test_pytorch.py::test_sparse_output__batched[6-3-pytorch_x_value_gen-True]
+0.56s call     tests/experimental/ml/test_pytorch.py::test_batching__empty_query_result[6-3-pytorch_x_value_gen-False]
+0.55s call     tests/experimental/ml/test_pytorch.py::test_batching__empty_query_result[6-3-pytorch_x_value_gen-True]
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+0.53s call     tests/test_get_anndata.py::test_get_anndata_wrong_layer_names
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+0.43s call     tests/test_open.py::test_can_open_with_anonymous_access
+0.35s call     tests/test_open.py::test_opening_census_without_anon_access_fails_with_bogus_creds
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+0.22s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-1-101]
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+0.18s setup    tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__batched[6-3-pytorch_x_value_gen-False]
+0.18s setup    tests/experimental/ml/test_pytorch.py::test__X_tensor_dtype_matches_X_matrix[6-3-pytorch_x_value_gen-True]
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+0.18s setup    tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__batched[6-3-pytorch_x_value_gen-True]
+0.18s setup    tests/experimental/ml/test_pytorch.py::test_experiment_dataloader__non_batched[3-3-pytorch_x_value_gen-True]
+0.18s setup    tests/experimental/ml/test_pytorch.py::test_batching__exactly_one_batch[3-3-pytorch_x_value_gen-True]
+0.18s setup    tests/experimental/ml/test_pytorch.py::test_batching__exactly_one_batch[3-3-pytorch_x_value_gen-False]
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+0.18s setup    tests/experimental/ml/test_pytorch.py::test_encoders[3-3-pytorch_x_value_gen]
+0.18s setup    tests/experimental/ml/test_pytorch.py::test_batching__partial_soma_batches_are_concatenated[10-1-pytorch_x_value_gen-False]
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+0.17s setup    tests/experimental/ml/test_pytorch.py::test_batching__empty_query_result[6-3-pytorch_x_value_gen-False]
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+0.17s setup    tests/experimental/ml/test_pytorch.py::test_unique_soma_joinids[obs_range0-3-pytorch_x_value_gen-True]
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+0.17s setup    tests/experimental/ml/test_pytorch.py::test_batching__partial_soma_batches_are_concatenated[10-1-pytorch_x_value_gen-True]
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+0.16s call     tests/test_open.py::test_open_soma_errors
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+0.14s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-101-53]
+0.13s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-101-101]
+0.13s call     tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-101-101]
+0.13s call     tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-101-101]
+0.13s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-101-101]
+0.13s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-101-101]
+0.13s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-101-101]
+0.11s call     tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-11-53]
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+0.11s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-11-53]
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+0.11s call     tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-3-53]
+0.10s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-11-101]
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+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-11-53]
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+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-3-101]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-1-101]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-1-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_counts[100001-57-1-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-3-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-1-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-11-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_counts[100001-57-101-101]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-11-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-11-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_counts[100001-57-11-101]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-11-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-11-53]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-101-101]
+0.03s setup    tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-101-101]
+0.03s setup    tests/experimental/pp/test_online.py::test_mean[100001-57-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[True-0-100001-57-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[True-1-100001-57-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[True-100-100001-57-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar_nnz_only_batches_fails[100001-57]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-3-101]
+0.02s setup    tests/experimental/pp/test_online.py::test_mean[100001-57-101]
+0.02s setup    tests/experimental/pp/test_online.py::test_counts[100001-57-3-101]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-3-101]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-1-100001-57-1-53]
+0.02s call     tests/experimental/pp/test_online.py::test_counts[1200-511-101-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-0-100001-57-1-53]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-3-101]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-1-53]
+0.02s call     tests/experimental/pp/test_online.py::test_counts[1200-511-101-101]
+0.02s setup    tests/experimental/pp/test_online.py::test_meanvar[False-100-100001-57-3-53]
+0.02s call     tests/experimental/pp/test_online.py::test_mean[100001-57-53]
+0.01s call     tests/experimental/pp/test_online.py::test_mean[100001-57-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_counts[1200-511-11-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-100-1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[True-1-1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_counts[1200-511-11-101]
+0.01s call     tests/experimental/pp/test_online.py::test_counts[1200-511-3-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_counts[1200-511-3-101]
+0.01s call     tests/experimental/pp/test_online.py::test_counts[1200-511-1-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-1-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-1-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-1-1200-511-1-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-0-1200-511-1-101]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-1-53]
+0.01s call     tests/experimental/pp/test_online.py::test_meanvar[False-100-1200-511-1-101]
+0.01s call     tests/experimental/pp/test_online.py::test_counts[1200-511-1-101]
+0.01s setup    tests/experimental/pp/test_online.py::test_meanvar[True-0-1200-511-1-101]
+
+(739 durations < 0.005s hidden.  Use -vv to show these durations.)
+=========================== short test summary info ============================
+FAILED api/python/cellxgene_census/tests/test_open.py::test_open_soma_with_context
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_hvg.py::test_get_highly_variable_genes[homo_sapiens-Homo sapiens-is_primary_data == True-dataset_id-obs_coords4]
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-True-0]
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-True-1]
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-False-0]
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-True-False-1]
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-False-True-0]
+FAILED api/python/cellxgene_census/tests/experimental/pp/test_stats.py::test_mean_variance[homo_sapiens-is_primary_data == True-obs_coords3-False-True-1]
+====== 8 failed, 398 passed, 5 skipped, 95 warnings in 7698.98s (2:08:18) ======
+```
+
+Note: failures have been identified to be related to the tests themselves, so we won't block the LTS release.
 
 ### 2023-07-26
 
diff --git a/api/r/cellxgene.census/DESCRIPTION b/api/r/cellxgene.census/DESCRIPTION
index 30ee8d71d..1ff8e8383 100644
--- a/api/r/cellxgene.census/DESCRIPTION
+++ b/api/r/cellxgene.census/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: cellxgene.census
 Title: CZ CELLxGENE Discover Cell Census
-Version: 1.7.0
+Version: 1.8.0
 Authors@R: 
-    person("Chan Zuckerberg Initiative", email = "soma@chanzuckerberg.com",
+    person("Chan Zuckerberg Initiative Foundation", email = "soma@chanzuckerberg.com",
            role = c("aut", "cre", "cph", "fnd"))
 Description: API to facilitate the use of the CZ CELLxGENE Discover 
   Census. For more information about the API and the project visit
diff --git a/api/r/cellxgene.census/LICENSE.md b/api/r/cellxgene.census/LICENSE.md
index 262ff3530..e0b43e454 100644
--- a/api/r/cellxgene.census/LICENSE.md
+++ b/api/r/cellxgene.census/LICENSE.md
@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2023 Chan Zuckerberg Initiative
+Copyright (c) 2023 Chan Zuckerberg Initiative Foundation
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/api/r/cellxgene.census/docs/404.html b/api/r/cellxgene.census/docs/404.html
index 72b1d735f..89a4ee856 100644
--- a/api/r/cellxgene.census/docs/404.html
+++ b/api/r/cellxgene.census/docs/404.html
@@ -30,7 +30,7 @@
     
     <a class="navbar-brand me-2" href="index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -93,7 +93,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/LICENSE-text.html b/api/r/cellxgene.census/docs/LICENSE-text.html
index b691862f9..7581c155d 100644
--- a/api/r/cellxgene.census/docs/LICENSE-text.html
+++ b/api/r/cellxgene.census/docs/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -68,7 +68,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/LICENSE.html b/api/r/cellxgene.census/docs/LICENSE.html
index 7b8bb44f6..fdac25e64 100644
--- a/api/r/cellxgene.census/docs/LICENSE.html
+++ b/api/r/cellxgene.census/docs/LICENSE.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -61,7 +61,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
     </div>
 
 <div id="mit-license" class="section level1">
-<p>Copyright (c) 2023 Chan Zuckerberg Initiative</p>
+<p>Copyright (c) 2023 Chan Zuckerberg Initiative Foundation</p>
 <p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:</p>
 <p>The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.</p>
 <p>THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
@@ -71,7 +71,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/census_compute_over_X.html b/api/r/cellxgene.census/docs/articles/census_compute_over_X.html
index 93ccf9559..2babb5571 100644
--- a/api/r/cellxgene.census/docs/articles/census_compute_over_X.html
+++ b/api/r/cellxgene.census/docs/articles/census_compute_over_X.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="census_compute_over_X_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Computing on X using online (incremental) algorithms</h1>
             
@@ -301,7 +300,7 @@ <h2 id="counting-cells-grouped-by-dataset-and-gene">Counting cells grouped by da
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/census_dataset_presence.html b/api/r/cellxgene.census/docs/articles/census_dataset_presence.html
index 969a5b512..887fd8f7c 100644
--- a/api/r/cellxgene.census/docs/articles/census_dataset_presence.html
+++ b/api/r/cellxgene.census/docs/articles/census_dataset_presence.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="census_dataset_presence_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Genes measured in each cell (dataset presence matrix)</h1>
             
@@ -408,7 +407,7 @@ <h3 id="close-the-census">Close the census<a class="anchor" aria-label="anchor"
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/census_datasets.html b/api/r/cellxgene.census/docs/articles/census_datasets.html
index b73e95994..993d98917 100644
--- a/api/r/cellxgene.census/docs/articles/census_datasets.html
+++ b/api/r/cellxgene.census/docs/articles/census_datasets.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="census_datasets_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Census Datasets example</h1>
             
@@ -439,7 +438,6 @@ <h2 id="fetching-the-expression-data-from-a-single-dataset">Fetching the express
 <p>Create a query on the mouse experiment, “RNA” measurement, for the <code>dataset_id</code>.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="st"><a href="https://github.com/single-cell-data/TileDB-SOMA" class="external-link">"tiledbsoma"</a></span><span class="op">)</span></span>
-<span><span class="co">#&gt; Warning: package 'tiledbsoma' was built under R version 4.3.1</span></span>
 <span><span class="va">obs_query</span> <span class="op">&lt;-</span> <span class="va"><a href="https://rdrr.io/pkg/tiledbsoma/man/SOMAAxisQuery.html" class="external-link">SOMAAxisQuery</a></span><span class="op">$</span><span class="fu">new</span><span class="op">(</span></span>
 <span>  value_filter <span class="op">=</span> <span class="st">"dataset_id == '0bd1a1de-3aee-40e0-b2ec-86c7a30c7149'"</span></span>
 <span><span class="op">)</span></span>
@@ -448,18 +446,11 @@ <h2 id="fetching-the-expression-data-from-a-single-dataset">Fetching the express
 <span>  obs_query <span class="op">=</span> <span class="va">obs_query</span></span>
 <span><span class="op">)</span></span>
 <span><span class="va">dataset_seurat</span> <span class="op">&lt;-</span> <span class="va">expt_query</span><span class="op">$</span><span class="fu">to_seurat</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>counts <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt; The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,</span></span>
-<span><span class="co">#&gt; which was just loaded, will retire in October 2023.</span></span>
-<span><span class="co">#&gt; Please refer to R-spatial evolution reports for details, especially</span></span>
-<span><span class="co">#&gt; https://r-spatial.org/r/2023/05/15/evolution4.html.</span></span>
-<span><span class="co">#&gt; It may be desirable to make the sf package available;</span></span>
-<span><span class="co">#&gt; package maintainers should consider adding sf to Suggests:.</span></span>
-<span><span class="co">#&gt; The sp package is now running under evolution status 2</span></span>
-<span><span class="co">#&gt;      (status 2 uses the sf package in place of rgdal)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">dataset_seurat</span><span class="op">)</span></span>
 <span><span class="co">#&gt; An object of class Seurat </span></span>
 <span><span class="co">#&gt; 52392 features across 40220 samples within 1 assay </span></span>
-<span><span class="co">#&gt; Active assay: RNA (52392 features, 0 variable features)</span></span></code></pre></div>
+<span><span class="co">#&gt; Active assay: RNA (52392 features, 0 variable features)</span></span>
+<span><span class="co">#&gt;  2 layers present: counts, data</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="downloading-the-original-source-h5ad-file-of-a-dataset">Downloading the original source H5AD file of a dataset<a class="anchor" aria-label="anchor" href="#downloading-the-original-source-h5ad-file-of-a-dataset"></a>
@@ -499,7 +490,7 @@ <h3 id="close-the-census">Close the census<a class="anchor" aria-label="anchor"
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/census_query_extract.html b/api/r/cellxgene.census/docs/articles/census_query_extract.html
index 421338b78..2302a32bb 100644
--- a/api/r/cellxgene.census/docs/articles/census_query_extract.html
+++ b/api/r/cellxgene.census/docs/articles/census_query_extract.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="census_query_extract_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Querying and fetching the single-cell data and cell/gene metadata</h1>
             
@@ -352,7 +351,8 @@ <h2 id="querying-expression-data-as-seurat">Querying expression data as <code>Se
 <span><span class="va">seurat_obj</span></span>
 <span><span class="co">#&gt; An object of class Seurat </span></span>
 <span><span class="co">#&gt; 2 features across 2729 samples within 1 assay </span></span>
-<span><span class="co">#&gt; Active assay: RNA (2 features, 0 variable features)</span></span></code></pre></div>
+<span><span class="co">#&gt; Active assay: RNA (2 features, 0 variable features)</span></span>
+<span><span class="co">#&gt;  2 layers present: counts, data</span></span></code></pre></div>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">seurat_obj</span><span class="op">[[</span><span class="op">]</span><span class="op">]</span><span class="op">)</span></span>
 <span><span class="co">#&gt;                orig.ident nCount_RNA nFeature_RNA cell_type tissue_general  disease</span></span>
@@ -438,7 +438,7 @@ <h3 id="close-the-census">Close the census<a class="anchor" aria-label="anchor"
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_census_info.html b/api/r/cellxgene.census/docs/articles/comp_bio_census_info.html
index 79d106a1b..68135900a 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_census_info.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_census_info.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="comp_bio_census_info_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Learning about the CZ CELLxGENE Census</h1>
             
@@ -603,7 +602,7 @@ <h3 id="example-diseased-t-cells-in-human-tissues">Example: diseased T cells in
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html b/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html
index 2f51223df..d3698b857 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="comp_bio_data_integration_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Integrating multi-dataset slices of data with Seurat</h1>
             
@@ -282,15 +281,22 @@ <h4 id="integration-across-datasets-using-dataset_id-and-controlling-for-batch-u
 <span><span class="co">#&gt; Merging objects</span></span>
 <span><span class="co">#&gt; Finding neighborhoods</span></span>
 <span><span class="co">#&gt; Finding anchors</span></span>
-<span><span class="co">#&gt;  Found 7026 anchors</span></span>
+<span><span class="co">#&gt;  Found 7136 anchors</span></span>
 <span><span class="co">#&gt; Filtering anchors</span></span>
-<span><span class="co">#&gt;  Retained 4880 anchors</span></span>
+<span><span class="co">#&gt;  Retained 5048 anchors</span></span>
 <span><span class="va">seurat_obj.combined</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://satijalab.org/seurat/reference/IntegrateData.html" class="external-link">IntegrateData</a></span><span class="op">(</span>anchorset <span class="op">=</span> <span class="va">seurat_obj.anchors</span>, normalization.method <span class="op">=</span> <span class="st">"SCT"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; [1] 1</span></span>
+<span><span class="co">#&gt; Warning: Different cells and/or features from existing assay SCT</span></span>
+<span><span class="co">#&gt; [1] 2</span></span>
+<span><span class="co">#&gt; Warning: Different cells and/or features from existing assay SCT</span></span>
 <span><span class="co">#&gt; Merging dataset 1 into 2</span></span>
 <span><span class="co">#&gt; Extracting anchors for merged samples</span></span>
 <span><span class="co">#&gt; Finding integration vectors</span></span>
 <span><span class="co">#&gt; Finding integration vector weights</span></span>
-<span><span class="co">#&gt; Integrating data</span></span></code></pre></div>
+<span><span class="co">#&gt; Integrating data</span></span>
+<span><span class="co">#&gt; Warning: Assay integrated changing from Assay to SCTAssay</span></span>
+<span></span>
+<span><span class="co">#&gt; Warning: Different cells and/or features from existing assay SCT</span></span></code></pre></div>
 <p>And inspect the new results by UMAP.</p>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://mojaveazure.github.io/seurat-object/reference/DefaultAssay.html" class="external-link">DefaultAssay</a></span><span class="op">(</span><span class="va">seurat_obj.combined</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="st">"integrated"</span></span>
@@ -298,20 +304,20 @@ <h4 id="integration-across-datasets-using-dataset_id-and-controlling-for-batch-u
 <span><span class="co"># Run the standard workflow for visualization and clustering</span></span>
 <span><span class="va">seurat_obj.combined</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://satijalab.org/seurat/reference/RunPCA.html" class="external-link">RunPCA</a></span><span class="op">(</span><span class="va">seurat_obj.combined</span>, npcs <span class="op">=</span> <span class="fl">30</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="va">seurat_obj.combined</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://satijalab.org/seurat/reference/RunUMAP.html" class="external-link">RunUMAP</a></span><span class="op">(</span><span class="va">seurat_obj.combined</span>, reduction <span class="op">=</span> <span class="st">"pca"</span>, dims <span class="op">=</span> <span class="fl">1</span><span class="op">:</span><span class="fl">30</span><span class="op">)</span></span>
-<span><span class="co">#&gt; 10:26:29 UMAP embedding parameters a = 0.9922 b = 1.112</span></span>
-<span><span class="co">#&gt; 10:26:29 Read 10153 rows and found 30 numeric columns</span></span>
-<span><span class="co">#&gt; 10:26:29 Using Annoy for neighbor search, n_neighbors = 30</span></span>
-<span><span class="co">#&gt; 10:26:29 Building Annoy index with metric = cosine, n_trees = 50</span></span>
+<span><span class="co">#&gt; 17:32:58 UMAP embedding parameters a = 0.9922 b = 1.112</span></span>
+<span><span class="co">#&gt; 17:32:58 Read 10153 rows and found 30 numeric columns</span></span>
+<span><span class="co">#&gt; 17:32:58 Using Annoy for neighbor search, n_neighbors = 30</span></span>
+<span><span class="co">#&gt; 17:32:58 Building Annoy index with metric = cosine, n_trees = 50</span></span>
 <span><span class="co">#&gt; 0%   10   20   30   40   50   60   70   80   90   100%</span></span>
 <span><span class="co">#&gt; [----|----|----|----|----|----|----|----|----|----|</span></span>
 <span><span class="co">#&gt; **************************************************|</span></span>
-<span><span class="co">#&gt; 10:26:31 Writing NN index file to temp file /var/folders/9q/j_1zpqns7vz_s5n9l6_b943r0000gr/T//RtmpYMsCtV/file7cd6474698e7</span></span>
-<span><span class="co">#&gt; 10:26:31 Searching Annoy index using 1 thread, search_k = 3000</span></span>
-<span><span class="co">#&gt; 10:26:34 Annoy recall = 100%</span></span>
-<span><span class="co">#&gt; 10:26:35 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30</span></span>
-<span><span class="co">#&gt; 10:26:36 Initializing from normalized Laplacian + noise (using irlba)</span></span>
-<span><span class="co">#&gt; 10:26:36 Commencing optimization for 200 epochs, with 410804 positive edges</span></span>
-<span><span class="co">#&gt; 10:26:42 Optimization finished</span></span></code></pre></div>
+<span><span class="co">#&gt; 17:32:59 Writing NN index file to temp file /tmp/Rtmp91c2fH/file1306c933fc5688</span></span>
+<span><span class="co">#&gt; 17:32:59 Searching Annoy index using 1 thread, search_k = 3000</span></span>
+<span><span class="co">#&gt; 17:33:02 Annoy recall = 100%</span></span>
+<span><span class="co">#&gt; 17:33:02 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30</span></span>
+<span><span class="co">#&gt; 17:33:03 Initializing from normalized Laplacian + noise (using RSpectra)</span></span>
+<span><span class="co">#&gt; 17:33:03 Commencing optimization for 200 epochs, with 409718 positive edges</span></span>
+<span><span class="co">#&gt; 17:33:07 Optimization finished</span></span></code></pre></div>
 <p>Plot the UMAP.</p>
 <div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># By assay</span></span>
@@ -361,7 +367,7 @@ <h4 id="integration-across-datasets-using-dataset_id-and-controlling-for-batch-u
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_data_integration_files/figure-html/unnamed-chunk-12-1.png b/api/r/cellxgene.census/docs/articles/comp_bio_data_integration_files/figure-html/unnamed-chunk-12-1.png
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index d409967ad..aa061f69d 100644
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diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_data_integration_files/figure-html/unnamed-chunk-23-1.png b/api/r/cellxgene.census/docs/articles/comp_bio_data_integration_files/figure-html/unnamed-chunk-23-1.png
index bf40b7ff5..e250dc6ad 100644
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diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html b/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html
index 782e325c1..de553363f 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="comp_bio_normalizing_full_gene_sequencing_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Normalizing full-length gene sequencing data</h1>
             
@@ -168,7 +167,8 @@ <h2 id="normalizing-expression-to-account-for-gene-length">Normalizing expressio
 <code class="sourceCode R"><span><span class="va">liver_seurat</span></span>
 <span><span class="co">#&gt; An object of class Seurat </span></span>
 <span><span class="co">#&gt; 52392 features across 2859 samples within 1 assay </span></span>
-<span><span class="co">#&gt; Active assay: RNA (52392 features, 0 variable features)</span></span></code></pre></div>
+<span><span class="co">#&gt; Active assay: RNA (52392 features, 0 variable features)</span></span>
+<span><span class="co">#&gt;  2 layers present: counts, data</span></span></code></pre></div>
 <p>Let’s get the genes measured in this dataset.</p>
 <div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">liver_dataset_joinid</span> <span class="op">&lt;-</span> <span class="va">liver_dataset</span><span class="op">$</span><span class="va">soma_joinid</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span></span>
@@ -180,11 +180,16 @@ <h2 id="normalizing-expression-to-account-for-gene-length">Normalizing expressio
 <span><span class="va">liver_seurat</span></span>
 <span><span class="co">#&gt; An object of class Seurat </span></span>
 <span><span class="co">#&gt; 17992 features across 2859 samples within 1 assay </span></span>
-<span><span class="co">#&gt; Active assay: RNA (17992 features, 0 variable features)</span></span></code></pre></div>
+<span><span class="co">#&gt; Active assay: RNA (17992 features, 0 variable features)</span></span>
+<span><span class="co">#&gt;  2 layers present: counts, data</span></span></code></pre></div>
 <p>We can see that out of all genes in the Census <strong>17,992</strong> were measured in this dataset.</p>
 <p>Now let’s normalize these genes by gene length. We can easily do this because the Census has gene lengths included in the gene metadata under <code>feature_length</code>.</p>
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://mojaveazure.github.io/seurat-object/reference/AssayData.html" class="external-link">GetAssayData</a></span><span class="op">(</span><span class="va">liver_seurat</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span>, slot <span class="op">=</span> <span class="st">"data"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; Warning: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0.</span></span>
+<span><span class="co">#&gt; <span style="color: #00BBBB;">ℹ</span> Please use the `layer` argument instead.</span></span>
+<span><span class="co">#&gt; <span style="color: #555555;">This warning is displayed once every 8 hours.</span></span></span>
+<span><span class="co">#&gt; <span style="color: #555555;">Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.</span></span></span>
 <span><span class="co">#&gt; 5 x 5 sparse Matrix of class "dgCMatrix"</span></span>
 <span><span class="co">#&gt;                    cell3530079 cell3530080 cell3530081 cell3530082 cell3530083</span></span>
 <span><span class="co">#&gt; ENSMUSG00000025900           .           .           .           .           .</span></span>
@@ -259,7 +264,7 @@ <h2 id="validation-through-clustering-exploration">Validation through clustering
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing_files/figure-html/unnamed-chunk-15-1.png b/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing_files/figure-html/unnamed-chunk-15-1.png
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diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html b/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html
index 422b086eb..cb5d15dd3 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -84,8 +84,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
 
 
 
-
-<div class="row">
+<script src="comp_bio_summarize_axis_query_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Summarizing cell and gene metadata</h1>
             
@@ -265,7 +264,7 @@ <h3 id="close-the-census">Close the census<a class="anchor" aria-label="anchor"
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/articles/index.html b/api/r/cellxgene.census/docs/articles/index.html
index 4bd0bc39a..999b9f56d 100644
--- a/api/r/cellxgene.census/docs/articles/index.html
+++ b/api/r/cellxgene.census/docs/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -90,7 +90,7 @@ <h3>API</h3>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/authors.html b/api/r/cellxgene.census/docs/authors.html
index 0d4349fed..29e9b7f2b 100644
--- a/api/r/cellxgene.census/docs/authors.html
+++ b/api/r/cellxgene.census/docs/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -63,7 +63,7 @@ <h6 class="dropdown-header" data-toc-skip>cellxgene.census capabilities</h6>
       <h2>Authors</h2>
       
       <ul class="list-unstyled"><li>
-          <p><strong>Chan Zuckerberg Initiative</strong>. Author, maintainer, copyright holder, funder. 
+          <p><strong>Chan Zuckerberg Initiative Foundation</strong>. Author, maintainer, copyright holder, funder. 
           </p>
         </li>
       </ul></div>
@@ -72,15 +72,15 @@ <h2>Authors</h2>
       <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/chanzuckerberg/cellxgene-census/tree/main/api/r/cellxgene.census/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-      <p>Chan Zuckerberg Initiative (2023).
+      <p>Chan Zuckerberg Initiative Foundation (2023).
 <em>cellxgene.census: CZ CELLxGENE Discover Cell Census</em>.
-R package version 1.7.0, <a href="https://github.com/chanzuckerberg/cellxgene-census" class="external-link">https://github.com/chanzuckerberg/cellxgene-census</a>. 
+R package version 1.8.0, <a href="https://github.com/chanzuckerberg/cellxgene-census" class="external-link">https://github.com/chanzuckerberg/cellxgene-census</a>. 
 </p>
       <pre>@Manual{,
   title = {cellxgene.census: CZ CELLxGENE Discover Cell Census},
-  author = {{Chan Zuckerberg Initiative}},
+  author = {{Chan Zuckerberg Initiative Foundation}},
   year = {2023},
-  note = {R package version 1.7.0},
+  note = {R package version 1.8.0},
   url = {https://github.com/chanzuckerberg/cellxgene-census},
 }</pre>
     </div>
@@ -89,7 +89,7 @@ <h2 id="citation">Citation</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/deps/bootstrap-5.2.2/bootstrap.min.css b/api/r/cellxgene.census/docs/deps/bootstrap-5.2.2/bootstrap.min.css
index e764a5957..14537aa4b 100644
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+++ b/api/r/cellxgene.census/docs/deps/bootstrap-5.2.2/bootstrap.min.css
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var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse,.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-collapse{border-width:0}.accordion-flush .accordion-item,.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child,.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child,.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.bslib-sidebar-layout>.sidebar>.sidebar-content>.accordion .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: #6c757d;--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: #6c757d;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: .375rem;--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: #e9ecef;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(13,110,253,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: #6c757d;--bs-pagination-disabled-bg: #fff;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) 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1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{color:#595a5c;background-color:#f8f9fa}.list-group-item-default.list-group-item-action:hover,.list-group-item-default.list-group-item-action:focus{color:#595a5c;background-color:#dfe0e1}.list-group-item-default.list-group-item-action.active{color:#fff;background-color:#595a5c;border-color:#595a5c}.list-group-item-primary{color:#084298;background-color:#cfe2ff}.list-group-item-primary.list-group-item-action:hover,.list-group-item-primary.list-group-item-action:focus{color:#084298;background-color:#bacbe6}.list-group-item-primary.list-group-item-action.active{color:#fff;background-color:#084298;border-color:#084298}.list-group-item-secondary{color:#41464b;background-color:#e2e3e5}.list-group-item-secondary.list-group-item-action:hover,.list-group-item-secondary.list-group-item-action:focus{color:#41464b;background-color:#cbccce}.list-group-item-secondary.list-group-item-action.active{color:#fff;background-color:#41464b;border-color:#41464b}.list-group-item-success{color:#0f5132;background-color:#d1e7dd}.list-group-item-success.list-group-item-action:hover,.list-group-item-success.list-group-item-action:focus{color:#0f5132;background-color:#bcd0c7}.list-group-item-success.list-group-item-action.active{color:#fff;background-color:#0f5132;border-color:#0f5132}.list-group-item-info{color:#055160;background-color:#cff4fc}.list-group-item-info.list-group-item-action:hover,.list-group-item-info.list-group-item-action:focus{color:#055160;background-color:#badce3}.list-group-item-info.list-group-item-action.active{color:#fff;background-color:#055160;border-color:#055160}.list-group-item-warning{color:#664d03;background-color:#fff3cd}.list-group-item-warning.list-group-item-action:hover,.list-group-item-warning.list-group-item-action:focus{color:#664d03;background-color:#e6dbb9}.list-group-item-warning.list-group-item-action.active{color:#fff;background-color:#664d03;border-color:#664d03}.list-group-item-danger{color:#842029;background-color:#f8d7da}.list-group-item-danger.list-group-item-action:hover,.list-group-item-danger.list-group-item-action:focus{color:#842029;background-color:#dfc2c4}.list-group-item-danger.list-group-item-action.active{color:#fff;background-color:#842029;border-color:#842029}.list-group-item-light{color:#636464;background-color:#fefefe}.list-group-item-light.list-group-item-action:hover,.list-group-item-light.list-group-item-action:focus{color:#636464;background-color:#e5e5e5}.list-group-item-light.list-group-item-action.active{color:#fff;background-color:#636464;border-color:#636464}.list-group-item-dark{color:#141619;background-color:#d3d3d4}.list-group-item-dark.list-group-item-action:hover,.list-group-item-dark.list-group-item-action:focus{color:#141619;background-color:#bebebf}.list-group-item-dark.list-group-item-action.active{color:#fff;background-color:#141619;border-color:#141619}.btn-close{box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:#000;background:transparent url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e") center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:.5}.btn-close:hover{color:#000;text-decoration:none;opacity:.75}.btn-close:focus{outline:0;box-shadow:0 0 0 .25rem rgba(13,110,253,0.25);opacity:1}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:.25}.btn-close-white{filter:invert(1) grayscale(100%) brightness(200%)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255,255,255,0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: .375rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-toast-header-color: #6c757d;--bs-toast-header-bg: rgba(255,255,255,0.85);--bs-toast-header-border-color: rgba(0,0,0,0.05);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * 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var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: .375rem;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: #595a5c;--bs-alert-bg: #f8f9fa;--bs-alert-border-color: #f5f6f8}.alert-default .alert-link{color:#47484a}.alert-primary{--bs-alert-color: #084298;--bs-alert-bg: #cfe2ff;--bs-alert-border-color: #b6d4fe}.alert-primary .alert-link{color:#06357a}.alert-secondary{--bs-alert-color: #41464b;--bs-alert-bg: #e2e3e5;--bs-alert-border-color: #d3d6d8}.alert-secondary .alert-link{color:#34383c}.alert-success{--bs-alert-color: #0f5132;--bs-alert-bg: #d1e7dd;--bs-alert-border-color: #badbcc}.alert-success .alert-link{color:#0c4128}.alert-info{--bs-alert-color: #055160;--bs-alert-bg: #cff4fc;--bs-alert-border-color: #b6effb}.alert-info .alert-link{color:#04414d}.alert-warning{--bs-alert-color: #664d03;--bs-alert-bg: #fff3cd;--bs-alert-border-color: #ffecb5}.alert-warning .alert-link{color:#523e02}.alert-danger{--bs-alert-color: #842029;--bs-alert-bg: #f8d7da;--bs-alert-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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diff --git a/api/r/cellxgene.census/docs/index.html b/api/r/cellxgene.census/docs/index.html
index dc57749ee..780ebcce2 100644
--- a/api/r/cellxgene.census/docs/index.html
+++ b/api/r/cellxgene.census/docs/index.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -186,7 +186,7 @@ <h2 data-toc-skip>License</h2>
 <div class="developers">
 <h2 data-toc-skip>Developers</h2>
 <ul class="list-unstyled">
-<li>Chan Zuckerberg Initiative <br><small class="roles"> Author, maintainer, copyright holder, funder </small>  </li>
+<li>Chan Zuckerberg Initiative Foundation <br><small class="roles"> Author, maintainer, copyright holder, funder </small>  </li>
 </ul>
 </div>
 
@@ -196,7 +196,7 @@ <h2 data-toc-skip>Developers</h2>
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Chan Zuckerberg Initiative.</p>
+<p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/pkgdown.yml b/api/r/cellxgene.census/docs/pkgdown.yml
index f9850d38d..87f686a52 100644
--- a/api/r/cellxgene.census/docs/pkgdown.yml
+++ b/api/r/cellxgene.census/docs/pkgdown.yml
@@ -1,4 +1,4 @@
-pandoc: 2.11.3.2
+pandoc: 2.9.2.1
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
@@ -10,5 +10,5 @@ articles:
   comp_bio_data_integration: comp_bio_data_integration.html
   comp_bio_normalizing_full_gene_sequencing: comp_bio_normalizing_full_gene_sequencing.html
   comp_bio_summarize_axis_query: comp_bio_summarize_axis_query.html
-last_built: 2023-10-25T17:04Z
+last_built: 2023-11-15T16:55Z
 
diff --git a/api/r/cellxgene.census/docs/reference/download_source_h5ad.html b/api/r/cellxgene.census/docs/reference/download_source_h5ad.html
index 2a22ffd4f..b51c12236 100644
--- a/api/r/cellxgene.census/docs/reference/download_source_h5ad.html
+++ b/api/r/cellxgene.census/docs/reference/download_source_h5ad.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -111,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/get_census_version_description.html b/api/r/cellxgene.census/docs/reference/get_census_version_description.html
index 22e15b74a..22dc7f581 100644
--- a/api/r/cellxgene.census/docs/reference/get_census_version_description.html
+++ b/api/r/cellxgene.census/docs/reference/get_census_version_description.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -94,8 +94,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1 /cell-census/2023-07-25/soma/      us-west-2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                  h5ads.uri</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1 s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>               h5ads.relative_uri h5ads.s3_region do_not_delete  alias</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE stable</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>               h5ads.relative_uri h5ads.s3_region do_not_delete  lts  alias</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE TRUE stable</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   census_version</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1         stable</span>
 </code></pre></div>
@@ -105,7 +105,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/get_census_version_directory.html b/api/r/cellxgene.census/docs/reference/get_census_version_directory.html
index 3ff1e8c62..b4964105d 100644
--- a/api/r/cellxgene.census/docs/reference/get_census_version_directory.html
+++ b/api/r/cellxgene.census/docs/reference/get_census_version_directory.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -83,54 +83,64 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">get_census_version_directory</span><span class="op">(</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>            release_date release_build</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> stable                     2023-07-25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> latest                     2023-10-23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> latest                     2023-11-13</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15                 2023-05-15</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25                 2023-07-25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-09-25                 2023-09-25</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-03                 2023-10-03</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-09                 2023-10-09</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18                 2023-10-18</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23                 2023-10-23</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-30                 2023-10-30</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-06                 2023-11-06</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-13                 2023-11-13</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           soma.uri</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> stable     s3://cellxgene-data-public/cell-census/2023-07-25/soma/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> latest     s3://cellxgene-data-public/cell-census/2023-10-23/soma/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> latest     s3://cellxgene-data-public/cell-census/2023-11-13/soma/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15 s3://cellxgene-data-public/cell-census/2023-05-15/soma/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25 s3://cellxgene-data-public/cell-census/2023-07-25/soma/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-09-25 s3://cellxgene-data-public/cell-census/2023-09-25/soma/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-03 s3://cellxgene-data-public/cell-census/2023-10-03/soma/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-09 s3://cellxgene-data-public/cell-census/2023-10-09/soma/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18 s3://cellxgene-data-public/cell-census/2023-10-18/soma/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23 s3://cellxgene-data-public/cell-census/2023-10-23/soma/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-30 s3://cellxgene-data-public/cell-census/2023-10-30/soma/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-06 s3://cellxgene-data-public/cell-census/2023-11-06/soma/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-13 s3://cellxgene-data-public/cell-census/2023-11-13/soma/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                        soma.relative_uri soma.s3_region</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> stable     /cell-census/2023-07-25/soma/      us-west-2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> latest     /cell-census/2023-10-23/soma/      us-west-2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> latest     /cell-census/2023-11-13/soma/      us-west-2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15 /cell-census/2023-05-15/soma/      us-west-2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25 /cell-census/2023-07-25/soma/      us-west-2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-09-25 /cell-census/2023-09-25/soma/      us-west-2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-03 /cell-census/2023-10-03/soma/      us-west-2</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-09 /cell-census/2023-10-09/soma/      us-west-2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18 /cell-census/2023-10-18/soma/      us-west-2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23 /cell-census/2023-10-23/soma/      us-west-2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-30 /cell-census/2023-10-30/soma/      us-west-2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-06 /cell-census/2023-11-06/soma/      us-west-2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-13 /cell-census/2023-11-13/soma/      us-west-2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                                                           h5ads.uri</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> stable     s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> latest     s3://cellxgene-data-public/cell-census/2023-10-23/h5ads/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> latest     s3://cellxgene-data-public/cell-census/2023-11-13/h5ads/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15 s3://cellxgene-data-public/cell-census/2023-05-15/h5ads/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25 s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-09-25 s3://cellxgene-data-public/cell-census/2023-09-25/h5ads/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-03 s3://cellxgene-data-public/cell-census/2023-10-03/h5ads/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-09 s3://cellxgene-data-public/cell-census/2023-10-09/h5ads/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18 s3://cellxgene-data-public/cell-census/2023-10-18/h5ads/</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23 s3://cellxgene-data-public/cell-census/2023-10-23/h5ads/</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>                        h5ads.relative_uri h5ads.s3_region do_not_delete  alias</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> stable     /cell-census/2023-07-25/h5ads/       us-west-2          TRUE stable</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> latest     /cell-census/2023-10-23/h5ads/       us-west-2          TRUE latest</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15 /cell-census/2023-05-15/h5ads/       us-west-2          TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-09-25 /cell-census/2023-09-25/h5ads/       us-west-2         FALSE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-03 /cell-census/2023-10-03/h5ads/       us-west-2         FALSE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-09 /cell-census/2023-10-09/h5ads/       us-west-2         FALSE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18 /cell-census/2023-10-18/h5ads/       us-west-2         FALSE       </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23 /cell-census/2023-10-23/h5ads/       us-west-2          TRUE       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-30 s3://cellxgene-data-public/cell-census/2023-10-30/h5ads/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-06 s3://cellxgene-data-public/cell-census/2023-11-06/h5ads/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-13 s3://cellxgene-data-public/cell-census/2023-11-13/h5ads/</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>                        h5ads.relative_uri h5ads.s3_region do_not_delete   lts</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> stable     /cell-census/2023-07-25/h5ads/       us-west-2          TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> latest     /cell-census/2023-11-13/h5ads/       us-west-2         FALSE    NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15 /cell-census/2023-05-15/h5ads/       us-west-2          TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18 /cell-census/2023-10-18/h5ads/       us-west-2         FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23 /cell-census/2023-10-23/h5ads/       us-west-2          TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-30 /cell-census/2023-10-30/h5ads/       us-west-2         FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-06 /cell-census/2023-11-06/h5ads/       us-west-2         FALSE    NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-13 /cell-census/2023-11-13/h5ads/       us-west-2         FALSE    NA</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>             alias</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> stable     stable</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> latest     latest</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-05-15       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-07-25       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-18       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-23       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-10-30       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-06       </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2023-11-13       </span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
@@ -138,7 +148,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/get_presence_matrix.html b/api/r/cellxgene.census/docs/reference/get_presence_matrix.html
index 2294473ee..5665e1b2c 100644
--- a/api/r/cellxgene.census/docs/reference/get_presence_matrix.html
+++ b/api/r/cellxgene.census/docs/reference/get_presence_matrix.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -107,7 +107,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/get_seurat.html b/api/r/cellxgene.census/docs/reference/get_seurat.html
index 9ac3f4dd0..e24a592bd 100644
--- a/api/r/cellxgene.census/docs/reference/get_seurat.html
+++ b/api/r/cellxgene.census/docs/reference/get_seurat.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -162,7 +162,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/get_single_cell_experiment.html b/api/r/cellxgene.census/docs/reference/get_single_cell_experiment.html
index e45cdd4a3..529b3bf82 100644
--- a/api/r/cellxgene.census/docs/reference/get_single_cell_experiment.html
+++ b/api/r/cellxgene.census/docs/reference/get_single_cell_experiment.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -164,7 +164,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/get_source_h5ad_uri.html b/api/r/cellxgene.census/docs/reference/get_source_h5ad_uri.html
index 009b9b3e5..e025f20c3 100644
--- a/api/r/cellxgene.census/docs/reference/get_source_h5ad_uri.html
+++ b/api/r/cellxgene.census/docs/reference/get_source_h5ad_uri.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -109,7 +109,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/index.html b/api/r/cellxgene.census/docs/reference/index.html
index 8a5a899a5..bf8a1ec2c 100644
--- a/api/r/cellxgene.census/docs/reference/index.html
+++ b/api/r/cellxgene.census/docs/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -154,7 +154,7 @@ <h2 id="versioning-of-census-builds">Versioning of Census builds<a class="anchor
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/new_SOMATileDBContext_for_census.html b/api/r/cellxgene.census/docs/reference/new_SOMATileDBContext_for_census.html
index 0f92f782c..b977153e7 100644
--- a/api/r/cellxgene.census/docs/reference/new_SOMATileDBContext_for_census.html
+++ b/api/r/cellxgene.census/docs/reference/new_SOMATileDBContext_for_census.html
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -111,7 +111,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/reference/open_soma.html b/api/r/cellxgene.census/docs/reference/open_soma.html
index 3858fc04b..303844a3a 100644
--- a/api/r/cellxgene.census/docs/reference/open_soma.html
+++ b/api/r/cellxgene.census/docs/reference/open_soma.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">cellxgene.census</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.7.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -119,7 +119,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Chan Zuckerberg Initiative.</p>
+  <p></p><p>Developed by Chan Zuckerberg Initiative Foundation.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/api/r/cellxgene.census/docs/search.json b/api/r/cellxgene.census/docs/search.json
index e636c83a9..1e2d918a4 100644
--- a/api/r/cellxgene.census/docs/search.json
+++ b/api/r/cellxgene.census/docs/search.json
@@ -1 +1 @@
-[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 Chan Zuckerberg Initiative Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/census_compute_over_X.html","id":"incremental-mean-calculation","dir":"Articles","previous_headings":"","what":"Incremental mean calculation","title":"Computing on X using online (incremental) algorithms","text":"Many statistics, marginal means, easy calculate incrementally. Let’s begin query X$raw sparse matrix unnormalized read counts, return results shards incrementally accumulate read count gene, divide cell count get mean reads per cell gene. First define query - case slice obs axis cells specific tissue & sex value, genes var axis. query$X() method returns iterator results, Arrow Table. table contain sparse X data obs/var coordinates, using standard SOMA names: soma_data - X values (float32) soma_dim_0 - obs coordinate (int64) soma_dim_1 - var coordinate (int64) Important: X matrices joined var/obs axis DataFrames integer join “id” (aka soma_joinid). positionally indexed, given cell gene may soma_joinid value (e.g., large integer). words, given X value, soma_dim_0 corresponds soma_joinid obs dataframe, soma_dim_1 coordinate corresponds soma_joinid var dataframe. convenience, query class includes utility simplify operations query slices. query$indexer indexer used wrap output query$X(), converting soma_joinids positional indexing query results. Positions [0, N), N number results query given axis. Key points: expensive query read results - rather make multiple passes data, read perform multiple computations. default, data census indexed soma_joinid positionally.","code":"library(\"tiledbsoma\") library(\"cellxgene.census\") census <- open_soma()  query <- census$get(\"census_data\")$get(\"mus_musculus\")$axis_query(   measurement_name = \"RNA\",   obs_query = SOMAAxisQuery$new(value_filter = \"tissue=='brain' && sex=='male'\") )  genes_df <- query$var(column_names = c(\"feature_id\", \"feature_name\"))$concat() genes_df <- as.data.frame(genes_df) n_genes <- nrow(genes_df)  # accumulator vector (for each gene) for the total count over all cells in X(\"raw\") raw_sum_by_gene <- numeric(n_genes) names(raw_sum_by_gene) <- genes_df$feature_id  # iterate through in-memory shards of query results tables <- query$X(\"raw\")$tables() while (!tables$read_complete()) {   table_part <- tables$read_next()   # table_part is an Arrow table with the columns mentioned above. The result   # order is not guaranteed!    # table_part$soma_dim_1 is the var/gene soma_joinid. But note that these are   # arbitrary int64 id's, and moreover each table_part may exhibit only a subset   # of the values we'll see over all query results. query$indexer helps us map   # any given soma_dim_1 values onto positions in query$var() (genes_df), that is   # the union of all values we'll see.   gene_indexes <- query$indexer$by_var(table_part$soma_dim_1)$as_vector()   stopifnot(sum(gene_indexes >= n_genes) == 0)   # sum(table_part) group by gene, yielding a numeric vector with the gene_index   # in its names   sum_part <- tapply(as.vector(table_part$soma_data), gene_indexes, sum)   # update the accumulator vector   which_genes <- as.integer(names(sum_part)) + 1  # nb: gene_indexes is zero-based   stopifnot(sum(which_genes > n_genes) == 0)   raw_sum_by_gene[which_genes] <- raw_sum_by_gene[which_genes] + sum_part }  # Divide each sum by cell count to get mean reads per cell (for each gene), # implicitly averaging in all zero entries in X even though they weren't included # in the sparse query results. genes_df$raw_mean <- raw_sum_by_gene / query$n_obs genes_df #>            feature_id  feature_name     raw_mean #> 1  ENSMUSG00000051951          Xkr4 1.283861e+00 #> 2  ENSMUSG00000089699        Gm1992 0.000000e+00 #> 3  ENSMUSG00000102343       Gm37381 0.000000e+00 #> 4  ENSMUSG00000025900           Rp1 2.914160e-01 #> 5  ENSMUSG00000025902         Sox17 6.074154e+01 #> 6  ENSMUSG00000104328       Gm37323 5.701742e-05 #> 7  ENSMUSG00000033845        Mrpl15 3.621738e+01 #> 8  ENSMUSG00000025903        Lypla1 1.827366e+01 #> 9  ENSMUSG00000104217       Gm37988 0.000000e+00 #> 10 ENSMUSG00000033813         Tcea1 3.960339e+01 #> 11 ENSMUSG00000002459         Rgs20 3.212989e+00 #> 12 ENSMUSG00000085623       Gm16041 5.701742e-05 #> 13 ENSMUSG00000033793       Atp6v1h 6.866793e+01 #> 14 ENSMUSG00000025905         Oprk1 4.198763e-01 #> 15 ENSMUSG00000033774        Npbwr1 1.140348e-04 #> 16 ENSMUSG00000025907        Rb1cc1 3.342227e+01 #> 17 ENSMUSG00000090031 4732440D04Rik 1.317102e-02 #> 18 ENSMUSG00000087247        Alkal1 5.701742e-05 #> 19 ENSMUSG00000033740          St18 1.525501e+01 #> 20 ENSMUSG00000051285        Pcmtd1 4.981224e+01 #> 21 ENSMUSG00000097797       Gm26901 4.276306e-04 #> 22 ENSMUSG00000103067       Gm30414 0.000000e+00 #> 23 ENSMUSG00000025909         Sntg1 1.083131e+00 #> 24 ENSMUSG00000061024          Rrs1 1.929504e+01 #> 25 ENSMUSG00000025911        Adhfe1 1.163515e+01 #> 26 ENSMUSG00000067879           Vxn 9.911623e+00 #> 27 ENSMUSG00000099827       Gm29520 0.000000e+00 #> 28 ENSMUSG00000025912         Mybl1 2.439775e-01 #> 29 ENSMUSG00000045210        Vcpip1 3.177732e+01 #> 30 ENSMUSG00000097893 1700034P13Rik 5.257291e-01 #> 31 ENSMUSG00000025915          Sgk3 1.850455e+01 #> 32 ENSMUSG00000046101        Mcmdc2 6.555578e-01 #> 33 ENSMUSG00000098234         Snhg6 6.245488e+00 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 52359 rows ]"},{"path":"/articles/census_compute_over_X.html","id":"counting-cells-grouped-by-dataset-and-gene","dir":"Articles","previous_headings":"","what":"Counting cells grouped by dataset and gene","title":"Computing on X using online (incremental) algorithms","text":"goal example count number cells nonzero reads, grouped gene Census dataset_id. result data frame dataset, gene, number cells nonzero reads dataset gene. multi-factor aggregation, ’ll take advantage dplyr routines instead lower-level vector indexer shown . presentation purposes, ’ll limit query four genes, can expanded genes easily. Don’t forget close census.","code":"library(\"dplyr\")  query <- census$get(\"census_data\")$get(\"mus_musculus\")$axis_query(   measurement_name = \"RNA\",   obs_query = SOMAAxisQuery$new(value_filter = \"tissue=='brain'\"),   var_query = SOMAAxisQuery$new(value_filter = \"feature_name %in% c('Malat1', 'Ptprd', 'Dlg2', 'Pcdh9')\") )  obs_tbl <- query$obs(column_names=c(\"soma_joinid\", \"dataset_id\"))$concat() obs_df <- data.frame(   # materialize soma_joinid as character to avoid overflowing R 32-bit integer   cell_id = as.character(obs_tbl$soma_joinid),   dataset_id = obs_tbl$dataset_id$as_vector() ) var_tbl <- query$var(column_names=c(\"soma_joinid\", \"feature_name\"))$concat() var_df <- data.frame(   gene_id = as.character(var_tbl$soma_joinid),   feature_name = var_tbl$feature_name$as_vector() )  # accumulator for # cells by dataset & gene n_cells_grouped <- data.frame(   \"dataset_id\" = character(0),   \"gene_id\" = character(0),   \"n_cells\" = numeric(0) )  # iterate through in-memory shards of query results tables <- query$X(\"raw\")$tables() while (!tables$read_complete()) {   table_part <- tables$read_next()    # prepare a (dataset,gene,1) tuple for each entry in table_part   n_cells_part <- data.frame(     \"cell_id\" = as.character(table_part$soma_dim_0),     \"gene_id\" = as.character(table_part$soma_dim_1),     \"n_cells\" = 1   )   n_cells_part <- left_join(n_cells_part, obs_df, by = \"cell_id\")   stopifnot(sum(is.null(n_cells_part$dataset_id)) == 0)    # fold those into n_cells_grouped   n_cells_grouped <- n_cells_part %>%                      select(-cell_id) %>%                      bind_rows(n_cells_grouped) %>%                       group_by(dataset_id, gene_id) %>%                       summarise(n_cells = sum(n_cells)) %>%                       ungroup() }  # add gene names for display n_cells_grouped <- left_join(n_cells_grouped, var_df, by = \"gene_id\") stopifnot(sum(is.null(n_cells_grouped$feature_name)) == 0) n_cells_grouped[c(\"dataset_id\", \"feature_name\", \"n_cells\")] #> # A tibble: 17 × 3 #>    dataset_id                           feature_name n_cells #>    <chr>                                <chr>          <dbl> #>  1 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Dlg2           79513 #>  2 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Pcdh9          79476 #>  3 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Malat1         79667 #>  4 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Ptprd          79578 #>  5 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Dlg2              81 #>  6 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Pcdh9            125 #>  7 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Malat1         12622 #>  8 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Ptprd            474 #>  9 66ff82b4-9380-469c-bc4b-cfa08eacd325 Dlg2             856 #> 10 66ff82b4-9380-469c-bc4b-cfa08eacd325 Pcdh9           2910 #> 11 66ff82b4-9380-469c-bc4b-cfa08eacd325 Malat1          7102 #> 12 98e5ea9f-16d6-47ec-a529-686e76515e39 Dlg2             908 #> 13 98e5ea9f-16d6-47ec-a529-686e76515e39 Pcdh9           3027 #> 14 98e5ea9f-16d6-47ec-a529-686e76515e39 Malat1         20094 #> 15 c08f8441-4a10-4748-872a-e70c0bcccdba Dlg2              52 #> 16 c08f8441-4a10-4748-872a-e70c0bcccdba Pcdh9            117 #> 17 c08f8441-4a10-4748-872a-e70c0bcccdba Malat1         12992 census$close()"},{"path":"/articles/census_dataset_presence.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the Census","title":"Genes measured in each cell (dataset presence matrix)","text":"cellxgene.census R package contains convenient API open version Census (default, newest stable version).","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/census_dataset_presence.html","id":"fetching-the-ids-of-the-census-datasets","dir":"Articles","previous_headings":"","what":"Fetching the IDs of the Census datasets","title":"Genes measured in each cell (dataset presence matrix)","text":"Let’s grab table datasets included Census use table combination presence matrix .","code":"# Grab the experiment containing human data, and the measurement therein with RNA human <- census$get(\"census_data\")$get(\"homo_sapiens\") human_rna <- human$ms$get(\"RNA\")  # The census-wide datasets datasets_df <- as.data.frame(census$get(\"census_info\")$get(\"datasets\")$read()$concat()) print(datasets_df) #>    soma_joinid                        collection_id #> 1            0 e2c257e7-6f79-487c-b81c-39451cd4ab3c #> 2            1 e2c257e7-6f79-487c-b81c-39451cd4ab3c #> 3            2 e2c257e7-6f79-487c-b81c-39451cd4ab3c #> 4            3 f7cecffa-00b4-4560-a29a-8ad626b8ee08 #> 5            4 3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c #> 6            5 e5f58829-1a66-40b5-a624-9046778e74f5 #> 7            6 e5f58829-1a66-40b5-a624-9046778e74f5 #> 8            7 e5f58829-1a66-40b5-a624-9046778e74f5 #> 9            8 e5f58829-1a66-40b5-a624-9046778e74f5 #> 10           9 e5f58829-1a66-40b5-a624-9046778e74f5 #> 11          10 e5f58829-1a66-40b5-a624-9046778e74f5 #> 12          11 e5f58829-1a66-40b5-a624-9046778e74f5 #>                                                                                                                             collection_name #> 1                                                                      Spatial multiomics map of trophoblast development in early pregnancy #> 2                                                                      Spatial multiomics map of trophoblast development in early pregnancy #> 3                                                                      Spatial multiomics map of trophoblast development in early pregnancy #> 4                                       Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                            Tabula Sapiens #> 7                                                                                                                            Tabula Sapiens #> 8                                                                                                                            Tabula Sapiens #> 9                                                                                                                            Tabula Sapiens #> 10                                                                                                                           Tabula Sapiens #> 11                                                                                                                           Tabula Sapiens #> 12                                                                                                                           Tabula Sapiens #>                 collection_doi                           dataset_id #> 1   10.1038/s41586-023-05869-0 f171db61-e57e-4535-a06a-35d8b6ef8f2b #> 2   10.1038/s41586-023-05869-0 ecf2e08e-2032-4a9e-b466-b65b395f4a02 #> 3   10.1038/s41586-023-05869-0 74cff64f-9da9-4b2a-9b3b-8a04a1598040 #> 4  10.1016/j.ccell.2022.11.001 5af90777-6760-4003-9dba-8f945fec6fdf #> 5  10.1016/j.ccell.2021.03.007 bd65a70f-b274-4133-b9dd-0d1431b6af34 #> 6      10.1126/science.abl4896 ff45e623-7f5f-46e3-b47d-56be0341f66b #> 7      10.1126/science.abl4896 f01bdd17-4902-40f5-86e3-240d66dd2587 #> 8      10.1126/science.abl4896 e6a11140-2545-46bc-929e-da243eed2cae #> 9      10.1126/science.abl4896 e5c63d94-593c-4338-a489-e1048599e751 #> 10     10.1126/science.abl4896 d8732da6-8d1d-42d9-b625-f2416c30054b #> 11     10.1126/science.abl4896 d77ec7d6-ef2e-49d6-9e79-05b7f8881484 #> 12     10.1126/science.abl4896 cee11228-9f0b-4e57-afe2-cfe15ee56312 #>                                                                                                                               dataset_title #> 1                                                                                                                    donor_p13_trophoblasts #> 2                                                                                                                   All donors trophoblasts #> 3                                                                                                      All donors all cell states (in vivo) #> 4                                                                      Single-cell transcriptomic datasets of Renal cell carcinoma patients #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                 Tabula Sapiens - Pancreas #> 7                                                                                                           Tabula Sapiens - Salivary_Gland #> 8                                                                                                                    Tabula Sapiens - Heart #> 9                                                                                                                  Tabula Sapiens - Bladder #> 10                                                                                                                 Tabula Sapiens - Trachea #> 11                                                                                                                Tabula Sapiens - Prostate #> 12                                                                                                                  Tabula Sapiens - Spleen #>                            dataset_h5ad_path dataset_total_cell_count #> 1  f171db61-e57e-4535-a06a-35d8b6ef8f2b.h5ad                    31497 #> 2  ecf2e08e-2032-4a9e-b466-b65b395f4a02.h5ad                    67070 #> 3  74cff64f-9da9-4b2a-9b3b-8a04a1598040.h5ad                   286326 #> 4  5af90777-6760-4003-9dba-8f945fec6fdf.h5ad                   270855 #> 5  bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad                   167283 #> 6  ff45e623-7f5f-46e3-b47d-56be0341f66b.h5ad                    13497 #> 7  f01bdd17-4902-40f5-86e3-240d66dd2587.h5ad                    27199 #> 8  e6a11140-2545-46bc-929e-da243eed2cae.h5ad                    11505 #> 9  e5c63d94-593c-4338-a489-e1048599e751.h5ad                    24583 #> 10 d8732da6-8d1d-42d9-b625-f2416c30054b.h5ad                     9522 #> 11 d77ec7d6-ef2e-49d6-9e79-05b7f8881484.h5ad                    16375 #> 12 cee11228-9f0b-4e57-afe2-cfe15ee56312.h5ad                    34004 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 581 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"fetching-the-dataset-presence-matrix","dir":"Articles","previous_headings":"","what":"Fetching the dataset presence matrix","title":"Genes measured in each cell (dataset presence matrix)","text":"Now let’s fetch dataset presence matrix. convenience, read entire presence matrix (Homo sapiens) sparse matrix. convenience function providing capability: also need var dataframe, read R data frame convenient manipulation:","code":"presence_matrix <- get_presence_matrix(census, \"Homo sapiens\", \"RNA\") print(dim(presence_matrix)) #> NULL var_df <- as.data.frame(human_rna$var$read()$concat()) print(var_df) #>    soma_joinid      feature_id feature_name feature_length #> 1            0 ENSG00000121410         A1BG           3999 #> 2            1 ENSG00000268895     A1BG-AS1           3374 #> 3            2 ENSG00000148584         A1CF           9603 #> 4            3 ENSG00000175899          A2M           6318 #> 5            4 ENSG00000245105      A2M-AS1           2948 #> 6            5 ENSG00000166535        A2ML1           7156 #> 7            6 ENSG00000256661    A2ML1-AS1            452 #> 8            7 ENSG00000184389      A3GALT2           1023 #> 9            8 ENSG00000128274       A4GALT           3358 #> 10           9 ENSG00000118017        A4GNT           1779 #> 11          10 ENSG00000094914         AAAS           4727 #> 12          11 ENSG00000081760         AACS          16039 #> 13          12 ENSG00000114771        AADAC           1632 #> 14          13 ENSG00000197953      AADACL2           5071 #> 15          14 ENSG00000242908  AADACL2-AS1            635 #> 16          15 ENSG00000188984      AADACL3           4055 #> 17          16 ENSG00000204518      AADACL4           2027 #> 18          17 ENSG00000109576        AADAT           2970 #> 19          18 ENSG00000158122       PRXL2C           3098 #> 20          19 ENSG00000103591        AAGAB           4138 #> 21          20 ENSG00000115977         AAK1          24843 #> 22          21 ENSG00000087884        AAMDC           1943 #> 23          22 ENSG00000127837         AAMP           3055 #> 24          23 ENSG00000129673        AANAT           2156 #> 25          24 ENSG00000131043         AAR2           7291 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 60639 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"identifying-genes-measured-in-a-specific-dataset","dir":"Articles","previous_headings":"","what":"Identifying genes measured in a specific dataset","title":"Genes measured in each cell (dataset presence matrix)","text":"Now dataset table, genes metadata table, dataset presence matrix, can check gene set genes measured specific dataset. Important: presence matrix indexed soma_joinid, positionally indexed. words: first dimension presence matrix dataset’s soma_joinid, stored census_datasets dataframe. second dimension presence matrix feature’s soma_joinid, stored var dataframe. presence matrix method $take() lets slice soma_joinids census_datasets var. full presence matrix, slices , can exported regular matrix method $get_one_based_matrix() Let’s find gene \"ENSG00000286096\" measured dataset id \"97a17473-e2b1-4f31-a544-44a60773e2dd\".","code":"# Get soma_joinid for datasets and genes of interest var_joinid <- var_df$soma_joinid[var_df$feature_id == \"ENSG00000286096\"] dataset_joinid <- datasets_df$soma_joinid[datasets_df$dataset_id == \"97a17473-e2b1-4f31-a544-44a60773e2dd\"]  # Slice presence matrix with datasets and genes of interest presence_matrix_slice <- presence_matrix$take(i = dataset_joinid, j = var_joinid)  # Convert presence matrix to regular matrix presence_matrix_slice <- presence_matrix_slice$get_one_based_matrix()  # Find how if the gene is present in this dataset is_present <- presence_matrix_slice[, , drop = TRUE] cat(paste(\"Feature is\", if (is_present) \"present.\" else \"not present.\")) #> Feature is present."},{"path":"/articles/census_dataset_presence.html","id":"identifying-datasets-that-measured-specific-genes","dir":"Articles","previous_headings":"","what":"Identifying datasets that measured specific genes","title":"Genes measured in each cell (dataset presence matrix)","text":"Similarly, can determine datasets measured specific gene set genes.","code":"# Grab the feature's soma_joinid from the var dataframe var_joinid <- var_df$soma_joinid[var_df$feature_id == \"ENSG00000286096\"]  # The presence matrix is indexed by the joinids of the dataset and var dataframes, # so slice out the feature of interest by its joinid. presence_matrix_slice  <- presence_matrix$take(j = var_joinid)$get_one_based_matrix() measured_datasets <- presence_matrix_slice[, , drop = TRUE] != 0 dataset_joinids <- datasets_df$soma_joinid[measured_datasets]  # From the datasets dataframe, slice out the datasets which have a joinid in the list print(datasets_df[dataset_joinids, ]) #>    soma_joinid                        collection_id #> 5            4 3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c #> 6            5 e5f58829-1a66-40b5-a624-9046778e74f5 #> 7            6 e5f58829-1a66-40b5-a624-9046778e74f5 #> 8            7 e5f58829-1a66-40b5-a624-9046778e74f5 #> 9            8 e5f58829-1a66-40b5-a624-9046778e74f5 #> 11          10 e5f58829-1a66-40b5-a624-9046778e74f5 #> 12          11 e5f58829-1a66-40b5-a624-9046778e74f5 #> 14          13 e5f58829-1a66-40b5-a624-9046778e74f5 #> 15          14 e5f58829-1a66-40b5-a624-9046778e74f5 #> 19          18 e5f58829-1a66-40b5-a624-9046778e74f5 #> 20          19 e5f58829-1a66-40b5-a624-9046778e74f5 #> 21          20 e5f58829-1a66-40b5-a624-9046778e74f5 #>                                                                                                                             collection_name #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                            Tabula Sapiens #> 7                                                                                                                            Tabula Sapiens #> 8                                                                                                                            Tabula Sapiens #> 9                                                                                                                            Tabula Sapiens #> 11                                                                                                                           Tabula Sapiens #> 12                                                                                                                           Tabula Sapiens #> 14                                                                                                                           Tabula Sapiens #> 15                                                                                                                           Tabula Sapiens #> 19                                                                                                                           Tabula Sapiens #> 20                                                                                                                           Tabula Sapiens #> 21                                                                                                                           Tabula Sapiens #>                 collection_doi                           dataset_id #> 5  10.1016/j.ccell.2021.03.007 bd65a70f-b274-4133-b9dd-0d1431b6af34 #> 6      10.1126/science.abl4896 ff45e623-7f5f-46e3-b47d-56be0341f66b #> 7      10.1126/science.abl4896 f01bdd17-4902-40f5-86e3-240d66dd2587 #> 8      10.1126/science.abl4896 e6a11140-2545-46bc-929e-da243eed2cae #> 9      10.1126/science.abl4896 e5c63d94-593c-4338-a489-e1048599e751 #> 11     10.1126/science.abl4896 d77ec7d6-ef2e-49d6-9e79-05b7f8881484 #> 12     10.1126/science.abl4896 cee11228-9f0b-4e57-afe2-cfe15ee56312 #> 14     10.1126/science.abl4896 a2d4d33e-4c62-4361-b80a-9be53d2e50e8 #> 15     10.1126/science.abl4896 a0754256-f44b-4c4a-962c-a552e47d3fdc #> 19     10.1126/science.abl4896 6d41668c-168c-4500-b06a-4674ccf3e19d #> 20     10.1126/science.abl4896 5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c #> 21     10.1126/science.abl4896 55cf0ea3-9d2b-4294-871e-bb4b49a79fc7 #>                                                                                                                               dataset_title #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                 Tabula Sapiens - Pancreas #> 7                                                                                                           Tabula Sapiens - Salivary_Gland #> 8                                                                                                                    Tabula Sapiens - Heart #> 9                                                                                                                  Tabula Sapiens - Bladder #> 11                                                                                                                Tabula Sapiens - Prostate #> 12                                                                                                                  Tabula Sapiens - Spleen #> 14                                                                                                             Tabula Sapiens - Vasculature #> 15                                                                                                                     Tabula Sapiens - Eye #> 19                                                                                                                   Tabula Sapiens - Liver #> 20                                                                                                                     Tabula Sapiens - Fat #> 21                                                                                                                  Tabula Sapiens - Tongue #>                            dataset_h5ad_path dataset_total_cell_count #> 5  bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad                   167283 #> 6  ff45e623-7f5f-46e3-b47d-56be0341f66b.h5ad                    13497 #> 7  f01bdd17-4902-40f5-86e3-240d66dd2587.h5ad                    27199 #> 8  e6a11140-2545-46bc-929e-da243eed2cae.h5ad                    11505 #> 9  e5c63d94-593c-4338-a489-e1048599e751.h5ad                    24583 #> 11 d77ec7d6-ef2e-49d6-9e79-05b7f8881484.h5ad                    16375 #> 12 cee11228-9f0b-4e57-afe2-cfe15ee56312.h5ad                    34004 #> 14 a2d4d33e-4c62-4361-b80a-9be53d2e50e8.h5ad                    16037 #> 15 a0754256-f44b-4c4a-962c-a552e47d3fdc.h5ad                    10650 #> 19 6d41668c-168c-4500-b06a-4674ccf3e19d.h5ad                     5007 #> 20 5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c.h5ad                    20263 #> 21 55cf0ea3-9d2b-4294-871e-bb4b49a79fc7.h5ad                    15020 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 30 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"identifying-all-genes-measured-in-a-dataset","dir":"Articles","previous_headings":"","what":"Identifying all genes measured in a dataset","title":"Genes measured in each cell (dataset presence matrix)","text":"Finally, can find set genes measured cells given dataset.","code":"# Slice the dataset(s) of interest, and get the joinid(s) dataset_joinids <- datasets_df$soma_joinid[datasets_df$collection_id == \"17481d16-ee44-49e5-bcf0-28c0780d8c4a\"]  # Slice the presence matrix by the first dimension, i.e., by dataset presence_matrix_slice <- presence_matrix$take(i = dataset_joinids)$get_one_based_matrix() genes_measured <- Matrix::colSums(presence_matrix_slice) > 0 var_joinids <- var_df$soma_joinid[genes_measured]  print(var_df[var_joinids, ]) #>    soma_joinid      feature_id feature_name feature_length #> 1            0 ENSG00000121410         A1BG           3999 #> 2            1 ENSG00000268895     A1BG-AS1           3374 #> 3            2 ENSG00000148584         A1CF           9603 #> 4            3 ENSG00000175899          A2M           6318 #> 5            4 ENSG00000245105      A2M-AS1           2948 #> 6            5 ENSG00000166535        A2ML1           7156 #> 8            7 ENSG00000184389      A3GALT2           1023 #> 9            8 ENSG00000128274       A4GALT           3358 #> 10           9 ENSG00000118017        A4GNT           1779 #> 11          10 ENSG00000094914         AAAS           4727 #> 12          11 ENSG00000081760         AACS          16039 #> 13          12 ENSG00000114771        AADAC           1632 #> 14          13 ENSG00000197953      AADACL2           5071 #> 16          15 ENSG00000188984      AADACL3           4055 #> 17          16 ENSG00000204518      AADACL4           2027 #> 18          17 ENSG00000109576        AADAT           2970 #> 19          18 ENSG00000158122       PRXL2C           3098 #> 20          19 ENSG00000103591        AAGAB           4138 #> 21          20 ENSG00000115977         AAK1          24843 #> 22          21 ENSG00000087884        AAMDC           1943 #> 23          22 ENSG00000127837         AAMP           3055 #> 24          23 ENSG00000129673        AANAT           2156 #> 25          24 ENSG00000131043         AAR2           7291 #> 26          25 ENSG00000205002         AARD           2404 #> 27          26 ENSG00000090861        AARS1          10248 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 27185 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"close-the-census","dir":"Articles","previous_headings":"Identifying all genes measured in a dataset","what":"Close the census","title":"Genes measured in each cell (dataset presence matrix)","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/articles/census_datasets.html","id":"fetching-the-datasets-table","dir":"Articles","previous_headings":"","what":"Fetching the datasets table","title":"Census Datasets example","text":"Census contains top-level data frame itemizing datasets contained therein. can read SOMADataFrame Arrow Table: R data frame: sum cell counts across datasets match number cells across SOMA experiments (human, mouse).","code":"library(\"cellxgene.census\") census <- open_soma() census_datasets <- census$get(\"census_info\")$get(\"datasets\")$read()$concat() print(census_datasets) #> Table #> 593 rows x 8 columns #> $soma_joinid <int64 not null> #> $collection_id <large_string not null> #> $collection_name <large_string not null> #> $collection_doi <large_string not null> #> $dataset_id <large_string not null> #> $dataset_title <large_string not null> #> $dataset_h5ad_path <large_string not null> #> $dataset_total_cell_count <int64 not null> census_datasets <- as.data.frame(census_datasets) print(census_datasets[, c(   \"dataset_id\",   \"dataset_title\",   \"dataset_total_cell_count\" )]) #>                              dataset_id #> 1  f171db61-e57e-4535-a06a-35d8b6ef8f2b #> 2  ecf2e08e-2032-4a9e-b466-b65b395f4a02 #> 3  74cff64f-9da9-4b2a-9b3b-8a04a1598040 #> 4  5af90777-6760-4003-9dba-8f945fec6fdf #> 5  bd65a70f-b274-4133-b9dd-0d1431b6af34 #> 6  ff45e623-7f5f-46e3-b47d-56be0341f66b #> 7  f01bdd17-4902-40f5-86e3-240d66dd2587 #> 8  e6a11140-2545-46bc-929e-da243eed2cae #> 9  e5c63d94-593c-4338-a489-e1048599e751 #> 10 d8732da6-8d1d-42d9-b625-f2416c30054b #> 11 d77ec7d6-ef2e-49d6-9e79-05b7f8881484 #> 12 cee11228-9f0b-4e57-afe2-cfe15ee56312 #> 13 a357414d-2042-4eb5-95f0-c58604a18bdd #> 14 a2d4d33e-4c62-4361-b80a-9be53d2e50e8 #> 15 a0754256-f44b-4c4a-962c-a552e47d3fdc #> 16 983d5ec9-40e8-4512-9e65-a572a9c486cb #> 17 7357cee7-9f7f-4ab0-8cec-90de8f047e38 #> 18 6ec405bb-4727-4c6d-ab4e-01fe489af7ea #> 19 6d41668c-168c-4500-b06a-4674ccf3e19d #> 20 5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c #> 21 55cf0ea3-9d2b-4294-871e-bb4b49a79fc7 #> 22 4f1555bc-4664-46c3-a606-78d34dd10d92 #> 23 2ba40233-8576-4dec-a5f1-2adfa115e2dc #> 24 2423ce2c-3149-4cca-a2ff-cf682ea29b5f #> 25 1c9eb291-6d31-47e1-96b2-129b5e1ae64f #> 26 18eb630b-a754-4111-8cd4-c24ec80aa5ec #> 27 0d2ee4ac-05ee-40b2-afb6-ebb584caa867 #> 28 0ced5e76-6040-47ff-8a72-93847965afc0 #> 29 0041b9c3-6a49-4bf7-8514-9bc7190067a7 #> 30 c8f83821-a242-4ed7-86e9-7da077f5d348 #> 31 c05e6940-729c-47bd-a2a6-6ce3730c4919 #> 32 894573ad-498f-47ee-9bec-ad0880147eea #> 33 84242d25-f656-4ca6-8e8d-f3d2beeba11f #> 34 3d044b52-140a-4528-bf0d-a2dbef9e1f40 #> 35 12194ced-8086-458e-84a8-e2ab935d8db1 #> 36 965386e9-1e4f-466d-bf59-ebdca4b66b9b #> 37 e47c65a8-7d2f-48b8-908e-04ea6505fa26 #> 38 c8d40d53-387b-48f2-9f89-72bfdb9c7c9f #> 39 a41202e6-173c-477c-8b4d-e0688ee1c4cb #> 40 7f08cbcc-5790-4576-8478-10e9a999b316 #> 41 3d0d6923-a2c4-476f-a53e-3b7bfa010d90 #> 42 2a7f90de-098a-4a6f-ac07-9e6462792412 #> 43 d7291f04-fbbb-4d65-990a-f01fa44e915b #> 44 8e10f1c4-8e98-41e5-b65f-8cd89a887122 #> 45 9d8e5dca-03a3-457d-b7fb-844c75735c83 #> 46 a6388a6f-6076-401b-9b30-7d4306a20035 #> 47 842c6f5d-4a94-4eef-8510-8c792d1124bc #> 48 74520626-b0ba-4ee9-86b5-714649554def #> 49 396a9124-fb20-4822-bf9c-e93fdf7c999a #> 50 e84f2780-51e8-4cfa-8aa0-13bbfef677c7 #> 51 dfdf1ae2-d624-4004-9353-f18b902f6bca #> 52 d1cbed97-d88f-4954-8925-13302fe30b39 #> 53 b03e4ef8-4e6b-47f4-84a7-e8ed033d08cd #> 54 f1f123cc-ca2c-460f-b7f1-88240efb1e82 #> 55 de94c504-4b58-4f42-b68d-74a8e4892f0e #> 56 da684768-fb01-455b-9f0f-b63a3e2f844f #> 57 9f222629-9e39-47d0-b83f-e08d610c7479 #> 58 b0e547f0-462b-4f81-b31b-5b0a5d96f537 #> 59 d41f45c1-1b7b-4573-a998-ac5c5acb1647 #> 60 f801b7a9-80a6-4d09-9161-71474deb58ae #> 61 be39785b-67cb-4177-be19-a40ee3747e45 #> 62 4c6f9f26-5470-455b-8933-c408232fbf56 #> 63 be35c935-ee4f-475c-9d3c-97630d59a735 #> 64 975e13b6-bec1-4eed-b46a-9be1f1357373 #> 65 24ec2dc5-3573-4d66-a9e1-25b7dcf43e27 #> 66 0ba636a1-4754-4786-a8be-7ab3cf760fd6 #> 67 c2876b1b-06d8-4d96-a56b-5304f815b99a #> 68 3f32121d-126b-4e8d-9f69-d86502d2a1b1 #> 69 be46dfdc-0f99-4731-8957-64ca37364985 #> 70 ac2fea99-ce08-4fca-8d03-a19f37bf21a3 #> 71 a13bda79-9134-46c9-9ed1-a2858be9aafe #> 72 5695d556-974e-4d92-9e99-5f61b8695313 #> 73 535e9336-2d8d-43c3-944d-bcbebe20df8a #> 74 4fb330ab-2d74-4649-b58f-7ffef457efdf #> 75 290d50c7-7158-4198-acf5-6d4b624fd3dc #> 76 18e2a8c5-33f7-455e-a58a-b2ba6921db27 #> 77 12967895-3d58-4e93-be2c-4e1bcf4388d5 #> 78 ee195b7d-184d-4dfa-9b1c-51a7e601ac11 #> 79 9968be68-ab65-4a38-9e1a-c9b6abece194 #> 80 b165f033-9dec-468a-9248-802fc6902a74 #> 81 d8da613f-e681-4c69-b463-e94f5e66847f #> 82 fe4b89d5-461e-440c-a5a8-621b37b122c0 #> 83 a37f857c-779f-464e-9310-3db43a1811e7 #> 84 6cf3634d-e911-44ad-bf52-c747a9af3c01 #> 85 5ce42b38-d867-487f-9b40-e8bb00b21d0b #>                                                                                                                                     dataset_title #> 1                                                                                                                          donor_p13_trophoblasts #> 2                                                                                                                         All donors trophoblasts #> 3                                                                                                            All donors all cell states (in vivo) #> 4                                                                            Single-cell transcriptomic datasets of Renal cell carcinoma patients #> 5        Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                       Tabula Sapiens - Pancreas #> 7                                                                                                                 Tabula Sapiens - Salivary_Gland #> 8                                                                                                                          Tabula Sapiens - Heart #> 9                                                                                                                        Tabula Sapiens - Bladder #> 10                                                                                                                       Tabula Sapiens - Trachea #> 11                                                                                                                      Tabula Sapiens - Prostate #> 12                                                                                                                        Tabula Sapiens - Spleen #> 13                                                                                                               Tabula Sapiens - Small_Intestine #> 14                                                                                                                   Tabula Sapiens - Vasculature #> 15                                                                                                                           Tabula Sapiens - Eye #> 16                                                                                                                         Tabula Sapiens - Blood #> 17                                                                                                               Tabula Sapiens - Large_Intestine #> 18                                                                                                                        Tabula Sapiens - Uterus #> 19                                                                                                                         Tabula Sapiens - Liver #> 20                                                                                                                           Tabula Sapiens - Fat #> 21                                                                                                                        Tabula Sapiens - Tongue #> 22                                                                                                                   Tabula Sapiens - Bone_Marrow #> 23                                                                                                                       Tabula Sapiens - Mammary #> 24                                                                                                                        Tabula Sapiens - Kidney #> 25                                                                                                                        Tabula Sapiens - Muscle #> 26                                                                                                                    Tabula Sapiens - Lymph_Node #> 27                                                                                                                          Tabula Sapiens - Lung #> 28                                                                                                                        Tabula Sapiens - Thymus #> 29                                                                                                                          Tabula Sapiens - Skin #> 30                                                                                                                      white matter - astrocytes #> 31                                                                                                                       white matter - all cells #> 32                                                                                                                         white matter - neurons #> 33                                                                                                                       white matter - microglia #> 34                                                                                                                  white matter - vascular cells #> 35                                                                                                                 white matter - oligodendroglia #> 36 Single cell RNA sequencing of bone marrow mononuclear cells from healthy donors and B-cell lymphoma patients following CD19 CAR T-cell therapy #> 37                                                                                                                scRNA-seq data - vascular cells #> 38                                                                                                            scRNA-seq data - perivascular cells #> 39                                                                                                               scRNA-seq data - lymphatic cells #> 40                                                                                                                     snRNA-seq data - all cells #> 41                                                                                                                   scRNA-seq data - fibroblasts #> 42                                                                                                              scRNA-seq data - epithelial cells #> 43                                                                               Single-cell RNA-seq for all cortical & hippocampal regions (10x) #> 44                                                                                                                                    All neurons #> 45                                                                                                                          22 integrated samples #> 46                                                                                                                 scRNA-seq data - myeloid cells #> 47                                                                                                                     scRNA-seq data - all cells #> 48                                                                                                                       scRNA-seq data - T cells #> 49                                                                                                                       scRNA-seq data - B cells #> 50                                                                                                             All cells from human liver dataset #> 51                                                                                                         Myeloid cells from human liver dataset #> 52                                                                                                   CD45 negative cells from human liver dataset #> 53                                                                                                        Lymphoid cells from human liver dataset #> 54                                                                                                 UMAP visualization of keratinocyte subclusters #> 55                                                                                                          UMAP visualization of all 12 datasets #> 56                                                                                                   UMAP visualization of fibroblast subclusters #> 57                                                                        An integrated cell atlas of the human lung in health and disease (full) #> 58                                         AIDA Phase 1: Chinese, Indian, Japanese, Korean, and Malay donors in Japan, Singapore, and South Korea #> 59           Transcriptional connectivity of regulatory T cells in the tumor microenvironment informs novel combination cancer therapy strategies #> 60          normal - Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response #> 61           ccRCC - Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response #> 62           chRCC - Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response #> 63                                                                                                                                  HBCA - stroma #> 64                                                                                                                              HBCA - epithelial #> 65                                                                                                                                  HBCA - immune #> 66                                                                                                                                  HBCA - global #> 67                                                                               Whole Taxonomy - MTG: Seattle Alzheimer's Disease Atlas (SEA-AD) #> 68                                                                                                 fetal and neonatal human small intestine cells #> 69                                                                                                                     Mouse Somatic Cell Lineage #> 70                                                                                                                      Human Immune Cell Lineage #> 71                                                                                                                                     Mouse Male #> 72                                                                                                                               Mouse Germ Cells #> 73                                                                                                                                     Human Male #> 74                                                                                                                     Human Somatic Cell Lineage #> 75                                                                                                                               Human Germ Cells #> 76                                                                                                                                   Human Female #> 77                                                                                                                                   Mouse Female #> 78                                                                                                                                 Adult duodenum #> 79                                                                                                                         Developing Human Atlas #> 80                                                                                                                         All non-neuronal cells #> 81                                                                                          A molecular single-cell lung atlas of lethal COVID-19 #> 82                                                                                                                                  TI epithelial #> 83                                                                                                                                      TI immune #> 84                                                                                                                                  colon stromal #> 85                                                                                                                               colon epithelial #>    dataset_total_cell_count #> 1                     31497 #> 2                     67070 #> 3                    286326 #> 4                    270855 #> 5                    167283 #> 6                     13497 #> 7                     27199 #> 8                     11505 #> 9                     24583 #> 10                     9522 #> 11                    16375 #> 12                    34004 #> 13                    12467 #> 14                    16037 #> 15                    10650 #> 16                    50115 #> 17                    13680 #> 18                     7124 #> 19                     5007 #> 20                    20263 #> 21                    15020 #> 22                    12297 #> 23                    11375 #> 24                     9641 #> 25                    30746 #> 26                    53275 #> 27                    35682 #> 28                    33664 #> 29                     9424 #> 30                     3596 #> 31                    45528 #> 32                    10734 #> 33                     3851 #> 34                     4880 #> 35                    21968 #> 36                    92676 #> 37                    83651 #> 38                    52638 #> 39                     8982 #> 40                   117346 #> 41                   208390 #> 42                   240804 #> 43                  1169213 #> 44                  2480956 #> 45                    72335 #> 46                    30789 #> 47                   714331 #> 48                    76567 #> 49                    12510 #> 50                   167598 #> 51                    40821 #> 52                    15481 #> 53                   105790 #> 54                     9471 #> 55                    15243 #> 56                     2303 #> 57                  2282447 #> 58                  1058909 #> 59                    82991 #> 60                     6044 #> 61                    20509 #> 62                     2576 #> 63                   413997 #> 64                   353167 #> 65                    23714 #> 66                   800198 #> 67                  1226855 #> 68                    36359 #> 69                    50933 #> 70                    20556 #> 71                    32889 #> 72                    10411 #> 73                   133811 #> 74                   191230 #> 75                    10993 #> 76                   213898 #> 77                    69709 #> 78                     5200 #> 79                   155232 #> 80                   888263 #> 81                   116313 #> 82                   154136 #> 83                   201072 #> 84                    39433 #> 85                    97788 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 508 rows ] census_data <- census$get(\"census_data\") all_experiments <- lapply(census_data$to_list(), function(x) census_data$get(x$name)) print(all_experiments) #> $mus_musculus #> <SOMAExperiment> #>   uri: s3://cellxgene-data-public/cell-census/2023-07-25/soma/census_data/mus_musculus  #>   arrays: obs*  #>   groups: ms*  #>  #> $homo_sapiens #> <SOMAExperiment> #>   uri: s3://cellxgene-data-public/cell-census/2023-07-25/soma/census_data/homo_sapiens  #>   arrays: obs*  #>   groups: ms* experiments_total_cells <- sum(sapply(all_experiments, function(x) {   nrow(x$obs$read(column_names = c(\"soma_joinid\"))$concat()) }))  print(paste(\"Found\", experiments_total_cells, \"cells in all experiments.\")) #> [1] \"Found 61656118 cells in all experiments.\" print(paste(   \"Found\", sum(as.vector(census_datasets$dataset_total_cell_count)),   \"cells in all datasets.\" )) #> [1] \"Found 61656118 cells in all datasets.\""},{"path":"/articles/census_datasets.html","id":"fetching-the-expression-data-from-a-single-dataset","dir":"Articles","previous_headings":"","what":"Fetching the expression data from a single dataset","title":"Census Datasets example","text":"Let’s pick one dataset slice census, turn Seurat -memory object. (requires Seurat package installed beforehand.) Create query mouse experiment, “RNA” measurement, dataset_id.","code":"census_datasets[census_datasets$dataset_id == \"0bd1a1de-3aee-40e0-b2ec-86c7a30c7149\", ] #>     soma_joinid                        collection_id    collection_name #> 523         522 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #>                collection_doi                           dataset_id #> 523 10.1038/s41586-020-2496-1 0bd1a1de-3aee-40e0-b2ec-86c7a30c7149 #>                                                                                        dataset_title #> 523 Bone marrow - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - 10x #>                             dataset_h5ad_path dataset_total_cell_count #> 523 0bd1a1de-3aee-40e0-b2ec-86c7a30c7149.h5ad                    40220 library(\"tiledbsoma\") #> Warning: package 'tiledbsoma' was built under R version 4.3.1 obs_query <- SOMAAxisQuery$new(   value_filter = \"dataset_id == '0bd1a1de-3aee-40e0-b2ec-86c7a30c7149'\" ) expt_query <- census_data$get(\"mus_musculus\")$axis_query(   measurement_name = \"RNA\",   obs_query = obs_query ) dataset_seurat <- expt_query$to_seurat(c(counts = \"raw\")) #> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package, #> which was just loaded, will retire in October 2023. #> Please refer to R-spatial evolution reports for details, especially #> https://r-spatial.org/r/2023/05/15/evolution4.html. #> It may be desirable to make the sf package available; #> package maintainers should consider adding sf to Suggests:. #> The sp package is now running under evolution status 2 #>      (status 2 uses the sf package in place of rgdal) print(dataset_seurat) #> An object of class Seurat  #> 52392 features across 40220 samples within 1 assay  #> Active assay: RNA (52392 features, 0 variable features)"},{"path":"/articles/census_datasets.html","id":"downloading-the-original-source-h5ad-file-of-a-dataset","dir":"Articles","previous_headings":"","what":"Downloading the original source H5AD file of a dataset","title":"Census Datasets example","text":"can use cellxgene.census::get_source_h5ad_uri() API fetch URI pointing H5AD associated dataset_id. H5AD can download CZ CELLxGENE Discover, may contain additional data-submitter provided information included Census. can fetch location cloud directly download system. local H5AD file can used R using SeuratDisk’s anndata converter.","code":"# Option 1: Direct download download_source_h5ad(   dataset_id = \"0bd1a1de-3aee-40e0-b2ec-86c7a30c7149\",   file = \"/tmp/Tabula_Muris_Senis-bone_marrow.h5ad\",   overwrite = TRUE ) # Option 2: Get location and download via preferred method get_source_h5ad_uri(\"0bd1a1de-3aee-40e0-b2ec-86c7a30c7149\") #> $uri #> [1] \"s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/0bd1a1de-3aee-40e0-b2ec-86c7a30c7149.h5ad\" #>  #> $s3_region #> [1] \"us-west-2\""},{"path":"/articles/census_datasets.html","id":"close-the-census","dir":"Articles","previous_headings":"Downloading the original source H5AD file of a dataset","what":"Close the census","title":"Census Datasets example","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/articles/census_query_extract.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the census","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"cellxgene.census R package contains convenient API open version Census (default, newest stable version). can learn cellxgene.census methods accessing corresponding documentation, example ?cellxgene.census::open_soma.","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/census_query_extract.html","id":"querying-cell-metadata-obs","dir":"Articles","previous_headings":"","what":"Querying cell metadata (obs)","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"human gene metadata Census, RNA assays, located census$get(\"census_data\")$get(\"homo_sapiens\")$obs. SOMADataFrame can materialized R data frame (tibble) using .data.frame(obs$read()$concat()). mouse cell metadata census$get(\"census_data\")$get(\"mus_musculus\").obs. slicing cell metadata two relevant arguments can passed read(): column_names — character vector indicating metadata columns fetch. Expressions one comparisons Comparisons one <column> <op> <value> <column> <op> <column> Expressions can combine comparisons using && || op one < | > | <= | >= | == | != %% learn metadata columns available fetching filtering can directly look keys cell metadata. soma_joinid special SOMADataFrame column used join operations. definition columns can found Census schema. can used fetch specific columns specific rows matching condition. latter need know values looking priori. example let’s see possible values available sex. can load cell metadata fetching column sex. can see three different values sex, \"male\", \"female\" \"unknown\". information can fetch cell metatadata specific sex value, example \"unknown\". can use column_names value_filter perform specific queries. example let’s fetch disease column cell_type \"B cell\" tissue_general \"lung\".","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$obs$colnames() #>  [1] \"soma_joinid\"                              #>  [2] \"dataset_id\"                               #>  [3] \"assay\"                                    #>  [4] \"assay_ontology_term_id\"                   #>  [5] \"cell_type\"                                #>  [6] \"cell_type_ontology_term_id\"               #>  [7] \"development_stage\"                        #>  [8] \"development_stage_ontology_term_id\"       #>  [9] \"disease\"                                  #> [10] \"disease_ontology_term_id\"                 #> [11] \"donor_id\"                                 #> [12] \"is_primary_data\"                          #> [13] \"self_reported_ethnicity\"                  #> [14] \"self_reported_ethnicity_ontology_term_id\" #> [15] \"sex\"                                      #> [16] \"sex_ontology_term_id\"                     #> [17] \"suspension_type\"                          #> [18] \"tissue\"                                   #> [19] \"tissue_ontology_term_id\"                  #> [20] \"tissue_general\"                           #> [21] \"tissue_general_ontology_term_id\" unique(as.data.frame(census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(column_names = \"sex\")$concat())) #>            sex #> 1      unknown #> 670     female #> 385438    male as.data.frame(census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(value_filter = \"sex == 'unknown'\")$concat()) #>    soma_joinid                           dataset_id     assay assay_ontology_term_id #> 1            0 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 2            1 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 3            2 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 4            3 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 5            4 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 6            5 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 7            6 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 8            7 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 9            8 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 10           9 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 11          10 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 12          11 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #>                        cell_type cell_type_ontology_term_id #> 1       syncytiotrophoblast cell                 CL:0000525 #> 2  placental villous trophoblast                 CL:2000060 #> 3       syncytiotrophoblast cell                 CL:0000525 #> 4       syncytiotrophoblast cell                 CL:0000525 #> 5       extravillous trophoblast                 CL:0008036 #> 6  placental villous trophoblast                 CL:2000060 #> 7       syncytiotrophoblast cell                 CL:0000525 #> 8       extravillous trophoblast                 CL:0008036 #> 9  placental villous trophoblast                 CL:2000060 #> 10      syncytiotrophoblast cell                 CL:0000525 #> 11      syncytiotrophoblast cell                 CL:0000525 #> 12      syncytiotrophoblast cell                 CL:0000525 #>                          development_stage development_stage_ontology_term_id disease #> 1  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 2  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 3  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 4  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 5  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 6  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 7  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 8  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 9  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 10 9th week post-fertilization human stage                     HsapDv:0000046  normal #> 11 9th week post-fertilization human stage                     HsapDv:0000046  normal #> 12 9th week post-fertilization human stage                     HsapDv:0000046  normal #>    disease_ontology_term_id  donor_id is_primary_data self_reported_ethnicity #> 1              PATO:0000461 P13_fetus           FALSE                 unknown #> 2              PATO:0000461 P13_fetus           FALSE                 unknown #> 3              PATO:0000461 P13_fetus           FALSE                 unknown #> 4              PATO:0000461 P13_fetus           FALSE                 unknown #> 5              PATO:0000461 P13_fetus           FALSE                 unknown #> 6              PATO:0000461 P13_fetus           FALSE                 unknown #> 7              PATO:0000461 P13_fetus           FALSE                 unknown #> 8              PATO:0000461 P13_fetus           FALSE                 unknown #> 9              PATO:0000461 P13_fetus           FALSE                 unknown #> 10             PATO:0000461 P13_fetus           FALSE                 unknown #> 11             PATO:0000461 P13_fetus           FALSE                 unknown #> 12             PATO:0000461 P13_fetus           FALSE                 unknown #>    self_reported_ethnicity_ontology_term_id     sex sex_ontology_term_id #> 1                                   unknown unknown              unknown #> 2                                   unknown unknown              unknown #> 3                                   unknown unknown              unknown #> 4                                   unknown unknown              unknown #> 5                                   unknown unknown              unknown #> 6                                   unknown unknown              unknown #> 7                                   unknown unknown              unknown #> 8                                   unknown unknown              unknown #> 9                                   unknown unknown              unknown #> 10                                  unknown unknown              unknown #> 11                                  unknown unknown              unknown #> 12                                  unknown unknown              unknown #>    suspension_type          tissue tissue_ontology_term_id tissue_general #> 1          nucleus decidua basalis          UBERON:0000453       placenta #> 2          nucleus decidua basalis          UBERON:0000453       placenta #> 3          nucleus decidua basalis          UBERON:0000453       placenta #> 4          nucleus decidua basalis          UBERON:0000453       placenta #> 5          nucleus decidua basalis          UBERON:0000453       placenta #> 6          nucleus decidua basalis          UBERON:0000453       placenta #> 7          nucleus decidua basalis          UBERON:0000453       placenta #> 8          nucleus decidua basalis          UBERON:0000453       placenta #> 9          nucleus decidua basalis          UBERON:0000453       placenta #> 10         nucleus decidua basalis          UBERON:0000453       placenta #> 11         nucleus decidua basalis          UBERON:0000453       placenta #> 12         nucleus decidua basalis          UBERON:0000453       placenta #>    tissue_general_ontology_term_id #> 1                   UBERON:0001987 #> 2                   UBERON:0001987 #> 3                   UBERON:0001987 #> 4                   UBERON:0001987 #> 5                   UBERON:0001987 #> 6                   UBERON:0001987 #> 7                   UBERON:0001987 #> 8                   UBERON:0001987 #> 9                   UBERON:0001987 #> 10                  UBERON:0001987 #> 11                  UBERON:0001987 #> 12                  UBERON:0001987 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 3251322 rows ] cell_metadata_b_cell <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   value_filter = \"cell_type == 'B cell' & tissue_general == 'lung'\",   column_names = \"disease\" )  cell_metadata_b_cell <- as.data.frame(cell_metadata_b_cell$concat())  table(cell_metadata_b_cell) #> disease #> chronic obstructive pulmonary disease                              COVID-19  #>                                  6369                                  2729  #>          hypersensitivity pneumonitis             interstitial lung disease  #>                                    52                                   376  #>                   lung adenocarcinoma             lung large cell carcinoma  #>                                 62351                                  1534  #>              lymphangioleiomyomatosis         non-small cell lung carcinoma  #>                                   133                                 17484  #>   non-specific interstitial pneumonia                                normal  #>                                   231                                 25461  #>                 pleomorphic carcinoma                             pneumonia  #>                                  1210                                    50  #>                   pulmonary emphysema                    pulmonary fibrosis  #>                                  1512                                  6798  #>                 pulmonary sarcoidosis             small cell lung carcinoma  #>                                     6                                   583  #>          squamous cell lung carcinoma  #>                                 11920"},{"path":"/articles/census_query_extract.html","id":"querying-gene-metadata-var","dir":"Articles","previous_headings":"","what":"Querying gene metadata (var)","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"human gene metadata Census located census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var. Similarly cell metadata, SOMADataFrame thus can also use method read(). mouse gene metadata census$get(\"census_data\")$get(\"mus_musculus\")$ms$get(\"RNA\")$var. Let’s take look metadata available column selection row filtering. exception soma_joinid columns defined Census schema. Similarly cell metadata, can use operations learn fetch gene metadata. example, get feature_name feature_length genes \"ENSG00000161798\" \"ENSG00000188229\" can following.","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var$colnames() #> [1] \"soma_joinid\"    \"feature_id\"     \"feature_name\"   \"feature_length\" var_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var$read(   value_filter = \"feature_id %in% c('ENSG00000161798', 'ENSG00000188229')\",   column_names = c(\"feature_name\", \"feature_length\") )  as.data.frame(var_df$concat()) #>   feature_name feature_length #> 1         AQP5           1884 #> 2       TUBB4B           2037"},{"path":"/articles/census_query_extract.html","id":"querying-expression-data-as-seurat","dir":"Articles","previous_headings":"","what":"Querying expression data as Seurat","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"convenient way query fetch expression data use get_seurat method cellxgene.census API. method combines column selection value filtering described obtain slices expression data based metadata queries. method return Seurat object, takes input census object, string organism, cell gene metadata can specify filters column selection described following arguments: obs_column_names — character vector indicating columns select cell metadata. obs_value_filter — expression selection conditions fetch cells meeting criteria. var_column_names — character vector indicating columns select gene metadata. var_value_filter — expression selection conditions fetch genes meeting criteria. example want fetch expression data : Genes \"ENSG00000161798\" \"ENSG00000188229\". \"B cells\" \"lung\" \"COVID-19\". gene metadata adding sex cell metadata. full description refer ?cellxgene.census::get_seurat.","code":"library(\"Seurat\")  seurat_obj <- get_seurat(   census, \"Homo sapiens\",   obs_column_names = c(\"cell_type\", \"tissue_general\", \"disease\", \"sex\"),   var_value_filter = \"feature_id %in% c('ENSG00000161798', 'ENSG00000188229')\",   obs_value_filter = \"cell_type == 'B cell' & tissue_general == 'lung' & disease == 'COVID-19'\" ) seurat_obj #> An object of class Seurat  #> 2 features across 2729 samples within 1 assay  #> Active assay: RNA (2 features, 0 variable features) head(seurat_obj[[]]) #>                orig.ident nCount_RNA nFeature_RNA cell_type tissue_general  disease #> cell5946018 SeuratProject          0            0    B cell           lung COVID-19 #> cell5948526 SeuratProject          1            1    B cell           lung COVID-19 #> cell5949180 SeuratProject          0            0    B cell           lung COVID-19 #> cell5949686 SeuratProject          0            0    B cell           lung COVID-19 #> cell5950730 SeuratProject          0            0    B cell           lung COVID-19 #> cell5952197 SeuratProject          0            0    B cell           lung COVID-19 #>                 sex #> cell5946018    male #> cell5948526 unknown #> cell5949180    male #> cell5949686 unknown #> cell5950730    male #> cell5952197 unknown head(seurat_obj$RNA[[]]) #>                 feature_name feature_length #> ENSG00000161798         AQP5           1884 #> ENSG00000188229       TUBB4B           2037"},{"path":"/articles/census_query_extract.html","id":"querying-expression-data-as-singlecellexperiment","dir":"Articles","previous_headings":"","what":"Querying expression data as SingleCellExperiment","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"Similarly previous section, get_single_cell_experiment method cellxgene.census API. behaves exactly get_seurat returns SingleCellExperiment object. example, repeat query can simply following. full description refer ?cellxgene.census::get_single_cell_experiment.","code":"library(\"SingleCellExperiment\")  sce_obj <- get_single_cell_experiment(   census, \"Homo sapiens\",   obs_column_names = c(\"cell_type\", \"tissue_general\", \"disease\", \"sex\"),   var_value_filter = \"feature_id %in% c('ENSG00000161798', 'ENSG00000188229')\",   obs_value_filter = \"cell_type == 'B cell' & tissue_general == 'lung' & disease == 'COVID-19'\" ) sce_obj #> class: SingleCellExperiment  #> dim: 2 2729  #> metadata(0): #> assays(1): counts #> rownames(2): ENSG00000161798 ENSG00000188229 #> rowData names(2): feature_name feature_length #> colnames(2729): obs5946018 obs5948526 ... obs48038140 obs48038164 #> colData names(4): cell_type tissue_general disease sex #> reducedDimNames(0): #> mainExpName: RNA #> altExpNames(0): head(colData(sce_obj)) #> DataFrame with 6 rows and 4 columns #>              cell_type tissue_general     disease         sex #>            <character>    <character> <character> <character> #> obs5946018      B cell           lung    COVID-19        male #> obs5948526      B cell           lung    COVID-19     unknown #> obs5949180      B cell           lung    COVID-19        male #> obs5949686      B cell           lung    COVID-19     unknown #> obs5950730      B cell           lung    COVID-19        male #> obs5952197      B cell           lung    COVID-19     unknown head(rowData(sce_obj)) #> DataFrame with 2 rows and 2 columns #>                 feature_name feature_length #>                  <character>      <integer> #> ENSG00000161798         AQP5           1884 #> ENSG00000188229       TUBB4B           2037"},{"path":"/articles/census_query_extract.html","id":"close-the-census","dir":"Articles","previous_headings":"Querying expression data as SingleCellExperiment","what":"Close the census","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/articles/comp_bio_census_info.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the Census","title":"Learning about the CZ CELLxGENE Census","text":"cellxgene.census R package contains convenient open_soma() API open version Census (stable default). can learn cellxgene.census methods accessing corresponding documentation, example ?cellxgene.census::open_soma.","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/comp_bio_census_info.html","id":"census-organization","dir":"Articles","previous_headings":"","what":"Census organization","title":"Learning about the CZ CELLxGENE Census","text":"Census schema defines structure Census. short, can think Census structured collection items stores different pieces information. items parent collection SOMA objects various types can accessed TileDB-SOMA API (documentation). cellxgene.census package contains convenient wrappers TileDB-SOMA API. example function used open Census: cellxgene_census.open_soma().","code":""},{"path":"/articles/comp_bio_census_info.html","id":"main-census-components","dir":"Articles","previous_headings":"Census organization","what":"Main Census components","title":"Learning about the CZ CELLxGENE Census","text":"command created census, SOMACollection, R6 class providing key-value associative map. get() method can access two top-level collection members, census_info census_data, instances SOMACollection.","code":""},{"path":"/articles/comp_bio_census_info.html","id":"census-summary-info","dir":"Articles","previous_headings":"Census organization","what":"Census summary info","title":"Learning about the CZ CELLxGENE Census","text":"census$get(\"census_info\")$get(\"summary\"): data frame high-level information Census, e.g. build date, total cell count, etc. census$get(\"census_info\")$get(\"datasets\"): data frame datasets CELLxGENE Discover used create Census. census$get(\"census_info\")$get(\"summary_cell_counts\"): data frame cell counts stratified relevant cell metadata Census data Data organism stored independent SOMAExperiment objects specialized form SOMACollection. store data matrix (cell genes), cell metadata, gene metadata, useful components covered notebook. data organized one organism – Homo sapiens: census$get(\"census_data\")$get(\"homo_sapiens\")$obs: Cell metadata census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\"): Data matrices, currently … census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$X$get(\"raw\"): matrix raw counts SOMASparseNDArray census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var: Gene Metadata","code":""},{"path":"/articles/comp_bio_census_info.html","id":"cell-metadata","dir":"Articles","previous_headings":"","what":"Cell metadata","title":"Learning about the CZ CELLxGENE Census","text":"can obtain cell metadata variables directly querying columns corresponding SOMADataFrame. variables can used querying Census case want work specific cells. variables defined CELLxGENE dataset schema except following: soma_joinid: SOMA-defined value use join operations. dataset_id: dataset id encoded census$get(\"census_info\")$get(\"datasets\"). tissue_general tissue_general_ontology_term_id: high-level tissue mapping.","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$obs$colnames() #>  [1] \"soma_joinid\"                              #>  [2] \"dataset_id\"                               #>  [3] \"assay\"                                    #>  [4] \"assay_ontology_term_id\"                   #>  [5] \"cell_type\"                                #>  [6] \"cell_type_ontology_term_id\"               #>  [7] \"development_stage\"                        #>  [8] \"development_stage_ontology_term_id\"       #>  [9] \"disease\"                                  #> [10] \"disease_ontology_term_id\"                 #> [11] \"donor_id\"                                 #> [12] \"is_primary_data\"                          #> [13] \"self_reported_ethnicity\"                  #> [14] \"self_reported_ethnicity_ontology_term_id\" #> [15] \"sex\"                                      #> [16] \"sex_ontology_term_id\"                     #> [17] \"suspension_type\"                          #> [18] \"tissue\"                                   #> [19] \"tissue_ontology_term_id\"                  #> [20] \"tissue_general\"                           #> [21] \"tissue_general_ontology_term_id\""},{"path":"/articles/comp_bio_census_info.html","id":"gene-metadata","dir":"Articles","previous_headings":"","what":"Gene metadata","title":"Learning about the CZ CELLxGENE Census","text":"Similarly, can obtain gene metadata variables directly querying columns corresponding SOMADataFrame. variables can use querying Census case specific genes interested . variables defined CELLxGENE dataset schema except following: soma_joinid: SOMA-defined value use join operations. feature_length: length base pairs gene.","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var$colnames() #> [1] \"soma_joinid\"    \"feature_id\"     \"feature_name\"   \"feature_length\""},{"path":"/articles/comp_bio_census_info.html","id":"census-summary-content-tables","dir":"Articles","previous_headings":"","what":"Census summary content tables","title":"Learning about the CZ CELLxGENE Census","text":"can take quick look high-level Census information looking census$get(\"census_info\")$get(\"summary\"): special interest label-value combinations : total_cell_count total number cells Census. unique_cell_count number unique cells, cells may present twice due meta-analysis consortia-like data. number_donors_homo_sapiens number_donors_mus_musculus number individuals human mouse. guaranteed unique one individual ID may present identical different datasets.","code":"as.data.frame(census$get(\"census_info\")$get(\"summary\")$read()$concat()) #>   soma_joinid                      label      value #> 1           0      census_schema_version      1.0.0 #> 2           1          census_build_date 2023-07-25 #> 3           2     dataset_schema_version      3.0.0 #> 4           3           total_cell_count   61656118 #> 5           4          unique_cell_count   37447773 #> 6           5 number_donors_homo_sapiens      13035 #> 7           6 number_donors_mus_musculus       1417"},{"path":"/articles/comp_bio_census_info.html","id":"cell-counts-by-cell-metadata","dir":"Articles","previous_headings":"Census summary content tables","what":"Cell counts by cell metadata","title":"Learning about the CZ CELLxGENE Census","text":"looking census$get(\"census_info)$get(\"summary_cell_counts\") can get general idea cell counts stratified relevant cell metadata. cell metadata included table, can take look cell gene metadata available sections “Cell metadata” “Gene metadata”. line retrieves table casts R data frame: combination organism values category cell metadata can take look total_cell_count unique_cell_count cell counts combination. values category specified ontology_term_id label, value’s IDs labels, respectively.","code":"census_counts <- as.data.frame(census$get(\"census_info\")$get(\"summary_cell_counts\")$read()$concat()) head(census_counts) #>   soma_joinid     organism category ontology_term_id unique_cell_count total_cell_count #> 1           0 Homo sapiens      all               na          33364242         56400873 #> 2           1 Homo sapiens    assay      EFO:0008722            264166           279635 #> 3           2 Homo sapiens    assay      EFO:0008780             25652            51304 #> 4           3 Homo sapiens    assay      EFO:0008919             89477           206754 #> 5           4 Homo sapiens    assay      EFO:0008931             78750           188248 #> 6           5 Homo sapiens    assay      EFO:0008953              4693             9386 #>        label #> 1         na #> 2   Drop-seq #> 3     inDrop #> 4   Seq-Well #> 5 Smart-seq2 #> 6   STRT-seq"},{"path":"/articles/comp_bio_census_info.html","id":"example-cell-metadata-included-in-the-summary-counts-table","dir":"Articles","previous_headings":"Census summary content tables > Cell counts by cell metadata","what":"Example: cell metadata included in the summary counts table","title":"Learning about the CZ CELLxGENE Census","text":"get available cell metadata summary counts table can following. Remember cell metadata available, variables omitted creation table.","code":"t(table(census_counts$organism, census_counts$category)) #>                           #>                           Homo sapiens Mus musculus #>   all                                1            1 #>   assay                             19            9 #>   cell_type                        613          248 #>   disease                           64            5 #>   self_reported_ethnicity           26            1 #>   sex                                3            3 #>   suspension_type                    1            1 #>   tissue                           220           66 #>   tissue_general                    54           27"},{"path":"/articles/comp_bio_census_info.html","id":"example-cell-counts-for-each-sequencing-assay-in-human-data","dir":"Articles","previous_headings":"Census summary content tables > Cell counts by cell metadata","what":"Example: cell counts for each sequencing assay in human data","title":"Learning about the CZ CELLxGENE Census","text":"get cell counts sequencing assay type human data, can perform following operations:","code":"human_assay_counts <- census_counts[census_counts$organism == \"Homo sapiens\" & census_counts$category == \"assay\", ] human_assay_counts <- human_assay_counts[order(human_assay_counts$total_cell_count, decreasing = TRUE), ]"},{"path":"/articles/comp_bio_census_info.html","id":"example-number-of-microglial-cells-in-the-census","dir":"Articles","previous_headings":"Census summary content tables > Cell counts by cell metadata","what":"Example: number of microglial cells in the Census","title":"Learning about the CZ CELLxGENE Census","text":"specific term categories shown can directly find number cells term.","code":"census_counts[census_counts$label == \"microglial cell\", ] #>      soma_joinid     organism  category ontology_term_id unique_cell_count #> 70            69 Homo sapiens cell_type       CL:0000129            268114 #> 1039        1038 Mus musculus cell_type       CL:0000129             48998 #>      total_cell_count           label #> 70             370771 microglial cell #> 1039            62617 microglial cell"},{"path":"/articles/comp_bio_census_info.html","id":"understanding-census-contents-beyond-the-summary-tables","dir":"Articles","previous_headings":"","what":"Understanding Census contents beyond the summary tables","title":"Learning about the CZ CELLxGENE Census","text":"using pre-computed tables census$get(\"census_info\") easy quick way understand contents Census, falls short want learn certain slices Census. example, may want learn : cell types available human liver? total number cells lung datasets stratified sequencing technology? sex distribution cells brain mouse? diseases available T cells? questions can answered directly querying cell metadata shown examples .","code":""},{"path":"/articles/comp_bio_census_info.html","id":"example-all-cell-types-available-in-human","dir":"Articles","previous_headings":"Understanding Census contents beyond the summary tables","what":"Example: all cell types available in human","title":"Learning about the CZ CELLxGENE Census","text":"exemplify process accessing slicing cell metadata summary stats, let’s start trivial example take look human cell types available Census: number rows total number cells humans. Now, wish get cell counts per cell type can work data frame. addition, focus cells marked is_primary_data=TRUE ensures de-duplicate cells appear CELLxGENE Discover. number unique cells. Now let’s look counts per cell type: shows abundant cell types “glutamatergic neuron”, “CD8-positive, alpha-beta T cell”, “CD4-positive, alpha-beta T cell”. Now let’s take look number unique cell types: total number different cell types human. information example can quickly obtained summary table census$get(\"census-info\")$get(\"summary_cell_counts\"). examples complex can achieved accessing cell metadata.","code":"obs_df <-   census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(column_names = c(\"cell_type\", \"is_primary_data\")) as.data.frame(obs_df$concat()) #>                         cell_type is_primary_data #> 1        syncytiotrophoblast cell           FALSE #> 2   placental villous trophoblast           FALSE #> 3        syncytiotrophoblast cell           FALSE #> 4        syncytiotrophoblast cell           FALSE #> 5        extravillous trophoblast           FALSE #> 6   placental villous trophoblast           FALSE #> 7        syncytiotrophoblast cell           FALSE #> 8        extravillous trophoblast           FALSE #> 9   placental villous trophoblast           FALSE #> 10       syncytiotrophoblast cell           FALSE #> 11       syncytiotrophoblast cell           FALSE #> 12       syncytiotrophoblast cell           FALSE #> 13  placental villous trophoblast           FALSE #> 14  placental villous trophoblast           FALSE #> 15       syncytiotrophoblast cell           FALSE #> 16       syncytiotrophoblast cell           FALSE #> 17       syncytiotrophoblast cell           FALSE #> 18       syncytiotrophoblast cell           FALSE #> 19       syncytiotrophoblast cell           FALSE #> 20       extravillous trophoblast           FALSE #> 21       syncytiotrophoblast cell           FALSE #> 22       syncytiotrophoblast cell           FALSE #> 23  placental villous trophoblast           FALSE #> 24       syncytiotrophoblast cell           FALSE #> 25       syncytiotrophoblast cell           FALSE #> 26       syncytiotrophoblast cell           FALSE #> 27       syncytiotrophoblast cell           FALSE #> 28       syncytiotrophoblast cell           FALSE #> 29       syncytiotrophoblast cell           FALSE #> 30       syncytiotrophoblast cell           FALSE #> 31  placental villous trophoblast           FALSE #> 32         trophoblast giant cell           FALSE #> 33       syncytiotrophoblast cell           FALSE #> 34  placental villous trophoblast           FALSE #> 35       syncytiotrophoblast cell           FALSE #> 36       extravillous trophoblast           FALSE #> 37  placental villous trophoblast           FALSE #> 38  placental villous trophoblast           FALSE #> 39       syncytiotrophoblast cell           FALSE #> 40       syncytiotrophoblast cell           FALSE #> 41       syncytiotrophoblast cell           FALSE #> 42       syncytiotrophoblast cell           FALSE #> 43       syncytiotrophoblast cell           FALSE #> 44  placental villous trophoblast           FALSE #> 45  placental villous trophoblast           FALSE #> 46       syncytiotrophoblast cell           FALSE #> 47  placental villous trophoblast           FALSE #> 48       syncytiotrophoblast cell           FALSE #> 49       syncytiotrophoblast cell           FALSE #> 50       syncytiotrophoblast cell           FALSE #> 51       syncytiotrophoblast cell           FALSE #> 52       syncytiotrophoblast cell           FALSE #> 53       syncytiotrophoblast cell           FALSE #> 54       syncytiotrophoblast cell           FALSE #> 55  placental villous trophoblast           FALSE #> 56  placental villous trophoblast           FALSE #> 57  placental villous trophoblast           FALSE #> 58       syncytiotrophoblast cell           FALSE #> 59       syncytiotrophoblast cell           FALSE #> 60       extravillous trophoblast           FALSE #> 61  placental villous trophoblast           FALSE #> 62       extravillous trophoblast           FALSE #> 63       syncytiotrophoblast cell           FALSE #> 64       syncytiotrophoblast cell           FALSE #> 65       syncytiotrophoblast cell           FALSE #> 66  placental villous trophoblast           FALSE #> 67       syncytiotrophoblast cell           FALSE #> 68  placental villous trophoblast           FALSE #> 69       syncytiotrophoblast cell           FALSE #> 70       syncytiotrophoblast cell           FALSE #> 71       syncytiotrophoblast cell           FALSE #> 72       syncytiotrophoblast cell           FALSE #> 73  placental villous trophoblast           FALSE #> 74  placental villous trophoblast           FALSE #> 75  placental villous trophoblast           FALSE #> 76  placental villous trophoblast           FALSE #> 77       syncytiotrophoblast cell           FALSE #> 78       syncytiotrophoblast cell           FALSE #> 79       syncytiotrophoblast cell           FALSE #> 80  placental villous trophoblast           FALSE #> 81       syncytiotrophoblast cell           FALSE #> 82       syncytiotrophoblast cell           FALSE #> 83       syncytiotrophoblast cell           FALSE #> 84  placental villous trophoblast           FALSE #> 85       extravillous trophoblast           FALSE #> 86       syncytiotrophoblast cell           FALSE #> 87       syncytiotrophoblast cell           FALSE #> 88  placental villous trophoblast           FALSE #> 89  placental villous trophoblast           FALSE #> 90       syncytiotrophoblast cell           FALSE #> 91       extravillous trophoblast           FALSE #> 92  placental villous trophoblast           FALSE #> 93       syncytiotrophoblast cell           FALSE #> 94       syncytiotrophoblast cell           FALSE #> 95       syncytiotrophoblast cell           FALSE #> 96       extravillous trophoblast           FALSE #> 97       syncytiotrophoblast cell           FALSE #> 98  placental villous trophoblast           FALSE #> 99  placental villous trophoblast           FALSE #> 100      extravillous trophoblast           FALSE #> 101 placental villous trophoblast           FALSE #> 102      syncytiotrophoblast cell           FALSE #> 103      syncytiotrophoblast cell           FALSE #> 104      extravillous trophoblast           FALSE #> 105 placental villous trophoblast           FALSE #> 106 placental villous trophoblast           FALSE #> 107 placental villous trophoblast           FALSE #> 108 placental villous trophoblast           FALSE #> 109      syncytiotrophoblast cell           FALSE #> 110 placental villous trophoblast           FALSE #> 111 placental villous trophoblast           FALSE #> 112      extravillous trophoblast           FALSE #> 113      extravillous trophoblast           FALSE #> 114      syncytiotrophoblast cell           FALSE #> 115 placental villous trophoblast           FALSE #> 116      extravillous trophoblast           FALSE #> 117 placental villous trophoblast           FALSE #> 118 placental villous trophoblast           FALSE #> 119      syncytiotrophoblast cell           FALSE #> 120      syncytiotrophoblast cell           FALSE #> 121      syncytiotrophoblast cell           FALSE #> 122      syncytiotrophoblast cell           FALSE #> 123      syncytiotrophoblast cell           FALSE #> 124 placental villous trophoblast           FALSE #> 125 placental villous trophoblast           FALSE #> 126 placental villous trophoblast           FALSE #> 127      extravillous trophoblast           FALSE #> 128      extravillous trophoblast           FALSE #>  [ reached 'max' / getOption(\"max.print\") -- omitted 56400745 rows ] obs_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   column_names = \"cell_type\",   value_filter = \"is_primary_data == TRUE\" )  obs_df <- as.data.frame(obs_df$concat()) nrow(obs_df) #> [1] 33364242 human_cell_type_counts <- table(obs_df$cell_type) sort(human_cell_type_counts, decreasing = TRUE)[1:10] #>  #>                          neuron            glutamatergic neuron  #>                         2673669                         1541605  #> CD4-positive, alpha-beta T cell CD8-positive, alpha-beta T cell  #>                         1258976                         1235987  #>              classical monocyte                          B cell  #>                         1030996                          908651  #>                     native cell             natural killer cell  #>                          889262                          768755  #>                      macrophage                 oligodendrocyte  #>                          721687                          710242 length(human_cell_type_counts) #> [1] 599"},{"path":"/articles/comp_bio_census_info.html","id":"example-cell-types-available-in-human-liver","dir":"Articles","previous_headings":"Understanding Census contents beyond the summary tables","what":"Example: cell types available in human liver","title":"Learning about the CZ CELLxGENE Census","text":"Similar example , can learn cell types available specific tissue, e.g. liver. achieve goal just need limit cell metadata tissue. use information cell metadata variable tissue_general. variable contains high-level tissue label cells Census: cell types cell counts human liver.","code":"obs_liver_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   column_names = \"cell_type\",   value_filter = \"is_primary_data == TRUE & tissue_general == 'liver'\" )  obs_liver_df <- as.data.frame(obs_liver_df$concat())  sort(table(obs_liver_df$cell_type), decreasing = TRUE)[1:10] #>  #>                          T cell                     hepatoblast  #>                           85739                           58447  #>                 neoplastic cell                    erythroblast  #>                           52431                           45605  #>                        monocyte                      hepatocyte  #>                           31388                           28309  #>             natural killer cell    periportal region hepatocyte  #>                           26871                           23509  #>                      macrophage centrilobular region hepatocyte  #>                           16707                           15819"},{"path":"/articles/comp_bio_census_info.html","id":"example-diseased-t-cells-in-human-tissues","dir":"Articles","previous_headings":"Understanding Census contents beyond the summary tables","what":"Example: diseased T cells in human tissues","title":"Learning about the CZ CELLxGENE Census","text":"example going get counts diseased cells annotated T cells. sake example focus “CD8-positive, alpha-beta T cell” “CD4-positive, alpha-beta T cell”: cell counts annotated indicated disease across human tissues “CD8-positive, alpha-beta T cell” “CD4-positive, alpha-beta T cell”.","code":"obs_t_cells_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   column_names = c(\"disease\", \"tissue_general\"),   value_filter = \"is_primary_data == TRUE & disease != 'normal' & cell_type %in% c('CD8-positive, alpha-beta T cell', 'CD4-positive, alpha-beta T cell')\" )  obs_t_cells_df <- as.data.frame(obs_t_cells_df$concat())  print(table(obs_t_cells_df)) #>                                        tissue_general #> disease                                 adipose tissue adrenal gland  blood blood clot #>   B-cell non-Hodgkin lymphoma                        0             0  62499          0 #>   breast cancer                                      0             0      0          0 #>   chronic obstructive pulmonary disease              0             0      0          0 #>   chronic rhinitis                                   0             0      0          0 #>   clear cell renal carcinoma                         0             0   6548          0 #>   COVID-19                                           0             0 819428          0 #>   Crohn disease                                      0             0      0          0 #>   cystic fibrosis                                    0             0      0          0 #>   Down syndrome                                      0             0      0          0 #>   follicular lymphoma                                0             0      0          0 #>   influenza                                          0             0   8871          0 #>   interstitial lung disease                          0             0      0          0 #>   kidney benign neoplasm                             0             0     20          0 #>   kidney oncocytoma                                  0             0     16          0 #>   lung adenocarcinoma                                0           205      0          0 #>                                        tissue_general #> disease                                 bone marrow  brain breast  colon kidney  liver #>   B-cell non-Hodgkin lymphoma                     0      0      0      0      0      0 #>   breast cancer                                   0      0   1850      0      0      0 #>   chronic obstructive pulmonary disease           0      0      0      0      0      0 #>   chronic rhinitis                                0      0      0      0      0      0 #>   clear cell renal carcinoma                      0      0      0      0  20540      0 #>   COVID-19                                        0      0      0      0      0      0 #>   Crohn disease                                   0      0      0  17490      0      0 #>   cystic fibrosis                                 0      0      0      0      0      0 #>   Down syndrome                                 181      0      0      0      0      0 #>   follicular lymphoma                             0      0      0      0      0      0 #>   influenza                                       0      0      0      0      0      0 #>   interstitial lung disease                       0      0      0      0      0      0 #>   kidney benign neoplasm                          0      0      0      0     10      0 #>   kidney oncocytoma                               0      0      0      0   2408      0 #>   lung adenocarcinoma                             0   3274      0      0      0    507 #>                                        tissue_general #> disease                                   lung lymph node   nose pleural fluid #>   B-cell non-Hodgkin lymphoma                0          0      0             0 #>   breast cancer                              0          0      0             0 #>   chronic obstructive pulmonary disease   9382          0      0             0 #>   chronic rhinitis                           0          0    909             0 #>   clear cell renal carcinoma                 0         36      0             0 #>   COVID-19                               30578          0     13             0 #>   Crohn disease                              0          0      0             0 #>   cystic fibrosis                            7          0      0             0 #>   Down syndrome                              0          0      0             0 #>   follicular lymphoma                        0       1089      0             0 #>   influenza                                  0          0      0             0 #>   interstitial lung disease               1803          0      0             0 #>   kidney benign neoplasm                     0          0      0             0 #>   kidney oncocytoma                          0          0      0             0 #>   lung adenocarcinoma                   215013      24969      0         11558 #>                                        tissue_general #> disease                                 respiratory system saliva small intestine #>   B-cell non-Hodgkin lymphoma                            0      0               0 #>   breast cancer                                          0      0               0 #>   chronic obstructive pulmonary disease                  0      0               0 #>   chronic rhinitis                                       0      0               0 #>   clear cell renal carcinoma                             0      0               0 #>   COVID-19                                               4     41               0 #>   Crohn disease                                          0      0           52029 #>   cystic fibrosis                                        0      0               0 #>   Down syndrome                                          0      0               0 #>   follicular lymphoma                                    0      0               0 #>   influenza                                              0      0               0 #>   interstitial lung disease                              0      0               0 #>   kidney benign neoplasm                                 0      0               0 #>   kidney oncocytoma                                      0      0               0 #>   lung adenocarcinoma                                    0      0               0 #>  [ reached getOption(\"max.print\") -- omitted 10 rows ]"},{"path":"/articles/comp_bio_data_integration.html","id":"finding-and-fetching-data-from-mouse-liver-10x-genomics-and-smart-seq2","dir":"Articles","previous_headings":"","what":"Finding and fetching data from mouse liver (10X Genomics and Smart-Seq2)","title":"Integrating multi-dataset slices of data with Seurat","text":"Let’s load packages needed notebook. Now can open Census. notebook use Tabula Muris Senis data liver contains cells 10X Genomics Smart-Seq2 technologies. Let’s query datasets table Census filtering collection_name “Tabula Muris Senis” dataset_title “liver”. Now can use values dataset_id query load Seurat object cells datasets. can check cell counts 10X Genomics Smart-Seq2 data looking assay metadata.","code":"library(\"cellxgene.census\") library(\"Seurat\") census <- open_soma() census_datasets <- census$get(\"census_info\")$get(\"datasets\") census_datasets <- census_datasets$read(value_filter = \"collection_name == 'Tabula Muris Senis'\") census_datasets <- as.data.frame(census_datasets$concat())  # Print rows with liver data census_datasets[grep(\"Liver\", census_datasets$dataset_title), ] #>    soma_joinid                        collection_id    collection_name #> 14         525 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #> 35         547 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #>               collection_doi                           dataset_id #> 14 10.1038/s41586-020-2496-1 4546e757-34d0-4d17-be06-538318925fcd #> 35 10.1038/s41586-020-2496-1 6202a243-b713-4e12-9ced-c387f8483dea #>                                                                                        dataset_title #> 14 Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2 #> 35        Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - 10x #>                            dataset_h5ad_path dataset_total_cell_count #> 14 4546e757-34d0-4d17-be06-538318925fcd.h5ad                     2859 #> 35 6202a243-b713-4e12-9ced-c387f8483dea.h5ad                     7294 tabula_muris_liver_ids <- c(\"4546e757-34d0-4d17-be06-538318925fcd\", \"6202a243-b713-4e12-9ced-c387f8483dea\")  seurat_obj <- get_seurat(   census,   organism = \"Mus musculus\",   obs_value_filter = \"dataset_id %in% tabula_muris_liver_ids\" ) table(seurat_obj$assay) #>  #>  10x 3' v2 Smart-seq2  #>       7294       2859"},{"path":"/articles/comp_bio_data_integration.html","id":"gene-length-normalization-of-smart-seq2-data-","dir":"Articles","previous_headings":"","what":"Gene-length normalization of Smart-Seq2 data.","title":"Integrating multi-dataset slices of data with Seurat","text":"Smart-seq2 read counts normalized gene length. Lets first get gene lengths var.feature_length. Now can use normalize Smart-seq data. let’s split object assay. normalize Smart-seq slice using gene lengths merge back single object.","code":"smart_seq_gene_lengths <- seurat_obj$RNA[[]]$feature_length seurat_obj.list <- SplitObject(seurat_obj, split.by = \"assay\") seurat_obj.list[[\"Smart-seq2\"]][[\"RNA\"]]@counts <- seurat_obj.list[[\"Smart-seq2\"]][[\"RNA\"]]@counts / smart_seq_gene_lengths seurat_obj <- merge(seurat_obj.list[[1]], seurat_obj.list[[2]])"},{"path":"/articles/comp_bio_data_integration.html","id":"integration-with-seurat","dir":"Articles","previous_headings":"","what":"Integration with Seurat","title":"Integrating multi-dataset slices of data with Seurat","text":"use native integration capabilities Seurat. comprehensive usage best practices Seurat intergation please refer doc site Seurat.","code":""},{"path":"/articles/comp_bio_data_integration.html","id":"inspecting-data-prior-to-integration","dir":"Articles","previous_headings":"Integration with Seurat","what":"Inspecting data prior to integration","title":"Integrating multi-dataset slices of data with Seurat","text":"Let’s take look strength batch effects data. perform embedding visualization via UMAP. Let’s basic data normalization variable gene selection now perform PCA UMAP   can see batch effects strong cells cluster primarily assay cell_type. Properly integrated embedding principle cluster primarily cell_type, assay best randomly distributed.","code":"seurat_obj <- SCTransform(seurat_obj) seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = \"vst\", nfeatures = 2000) seurat_obj <- RunPCA(seurat_obj, features = VariableFeatures(object = seurat_obj)) seurat_obj <- RunUMAP(seurat_obj, dims = 1:30) # By assay p1 <- DimPlot(seurat_obj, reduction = \"umap\", group.by = \"assay\") p1 # By cell type p2 <- DimPlot(seurat_obj, reduction = \"umap\", group.by = \"cell_type\") p2"},{"path":"/articles/comp_bio_data_integration.html","id":"data-integration-with-seurat","dir":"Articles","previous_headings":"Integration with Seurat","what":"Data integration with Seurat","title":"Integrating multi-dataset slices of data with Seurat","text":"Whenever query fetch Census data multiple datasets integration needs performed evidenced batch effects observed. paramaters Seurat used notebook selected model run quickly. best practices integration single-cell data using Seurat please refer documentation page. seurat_d reading article integrated cell atlas human lung health disease Sikkema et al. perfomed integration 43 datasets Lung. focus metadata Census can batch information integration.","code":""},{"path":"/articles/comp_bio_data_integration.html","id":"integration-across-datasets-using-dataset_id","dir":"Articles","previous_headings":"Integration with Seurat > Data integration with Seurat","what":"Integration across datasets using dataset_id","title":"Integrating multi-dataset slices of data with Seurat","text":"cells Census annotated dataset come \"dataset_id\". great place start integration. let’s run Seurat integration pipeline. First define model batch set dataset_id. Firs normalize select variable genes seperated batch key dataset_id Now perform integration. Let’s inspect results normalization UMAP visulization. plot UMAP.   Great! can see clustering longer mainly driven assay, albeit still contributing . Great! can see clustering longer mainly driven assay, albeit still contributing .","code":"# split the dataset into a list of two seurat objects for each dataset seurat_obj.list <- SplitObject(seurat_obj, split.by = \"dataset_id\")  # normalize each dataset independently seurat_obj.list <- lapply(X = seurat_obj.list, FUN = function(x) {   x <- SCTransform(x) })  # select features for integration features <- SelectIntegrationFeatures(object.list = seurat_obj.list) seurat_obj.list <- PrepSCTIntegration(seurat_obj.list, anchor.features = features) seurat_obj.anchors <- FindIntegrationAnchors(object.list = seurat_obj.list, anchor.features = features, normalization.method = \"SCT\") seurat_obj.combined <- IntegrateData(anchorset = seurat_obj.anchors, normalization.method = \"SCT\") DefaultAssay(seurat_obj.combined) <- \"integrated\"  # Run the standard workflow for visualization and clustering seurat_obj.combined <- ScaleData(seurat_obj.combined, verbose = FALSE) seurat_obj.combined <- RunPCA(seurat_obj.combined, npcs = 30, verbose = FALSE) seurat_obj.combined <- RunUMAP(seurat_obj.combined, reduction = \"pca\", dims = 1:30) # By assay p1 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"assay\") p1 # By cell type p2 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"cell_type\") p2"},{"path":"/articles/comp_bio_data_integration.html","id":"integration-across-datasets-using-dataset_id-and-controlling-for-batch-using-donor_id","dir":"Articles","previous_headings":"Integration with Seurat > Data integration with Seurat","what":"Integration across datasets using dataset_id and controlling for batch using donor_id","title":"Integrating multi-dataset slices of data with Seurat","text":"Similar dataset_id, cells Census annotated donor_id. definition donor_id depends dataset left discretion data curators. However still rich information can used batch variable integration. donor_id guaranteed unique across cells Census, strongly recommend concatenating dataset_id donor_id use batch separator Seurat Now perform integration. inspect new results UMAP. Plot UMAP.   can see using dataset_id donor_id batch cells now mostly cluster cell type.","code":"# split the dataset into a list of two seurat objects for each dataset seurat_obj.list <- SplitObject(seurat_obj, split.by = \"dataset_id\")  # normalize each dataset independently controlling for batch seurat_obj.list <- lapply(X = seurat_obj.list, FUN = function(x) {   x <- SCTransform(x, vars.to.regress = \"donor_id\") })  # select features for integration features <- SelectIntegrationFeatures(object.list = seurat_obj.list) seurat_obj.list <- PrepSCTIntegration(seurat_obj.list, anchor.features = features) seurat_obj.anchors <- FindIntegrationAnchors(object.list = seurat_obj.list, anchor.features = features, normalization.method = \"SCT\") #> Finding all pairwise anchors #> Running CCA #> Merging objects #> Finding neighborhoods #> Finding anchors #>  Found 7026 anchors #> Filtering anchors #>  Retained 4880 anchors seurat_obj.combined <- IntegrateData(anchorset = seurat_obj.anchors, normalization.method = \"SCT\") #> Merging dataset 1 into 2 #> Extracting anchors for merged samples #> Finding integration vectors #> Finding integration vector weights #> Integrating data DefaultAssay(seurat_obj.combined) <- \"integrated\"  # Run the standard workflow for visualization and clustering seurat_obj.combined <- RunPCA(seurat_obj.combined, npcs = 30, verbose = FALSE) seurat_obj.combined <- RunUMAP(seurat_obj.combined, reduction = \"pca\", dims = 1:30) #> 10:26:29 UMAP embedding parameters a = 0.9922 b = 1.112 #> 10:26:29 Read 10153 rows and found 30 numeric columns #> 10:26:29 Using Annoy for neighbor search, n_neighbors = 30 #> 10:26:29 Building Annoy index with metric = cosine, n_trees = 50 #> 0%   10   20   30   40   50   60   70   80   90   100% #> [----|----|----|----|----|----|----|----|----|----| #> **************************************************| #> 10:26:31 Writing NN index file to temp file /var/folders/9q/j_1zpqns7vz_s5n9l6_b943r0000gr/T//RtmpYMsCtV/file7cd6474698e7 #> 10:26:31 Searching Annoy index using 1 thread, search_k = 3000 #> 10:26:34 Annoy recall = 100% #> 10:26:35 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 #> 10:26:36 Initializing from normalized Laplacian + noise (using irlba) #> 10:26:36 Commencing optimization for 200 epochs, with 410804 positive edges #> 10:26:42 Optimization finished # By assay p1 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"assay\") p1 # By cell type p2 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"cell_type\") p2"},{"path":"/articles/comp_bio_data_integration.html","id":"integration-across-datasets-using-dataset_id-and-controlling-for-batch-using-donor_id-assay_ontology_term_id-suspension_type-","dir":"Articles","previous_headings":"Integration with Seurat > Data integration with Seurat","what":"Integration across datasets using dataset_id and controlling for batch using donor_id + assay_ontology_term_id + suspension_type.","title":"Integrating multi-dataset slices of data with Seurat","text":"cases one dataset may contain multiple assay types /multiple suspension types (cell vs nucleus), important consider metadata batches. Therefore, comprehensive definition batch Census can accomplished combining cell metadata dataset_id, donor_id, assay_ontology_term_id suspension_type, latter encode EFO ids assay types. example, two datasets used contain cells one assay , one suspension type . Thus make difference include metadata part batch. implementation look line","code":"# EXAMPLE, DON'T RUN.  # split the dataset into a list of seurat objects for each dataset seurat_obj.list <- SplitObject(seurat_obj, split.by = \"dataset_id\")  # normalize each dataset independently controlling for batch seurat_obj.list <- lapply(X = seurat_obj.list, FUN = function(x) {   x <- SCTransform(x, vars.to.regress = c(\"donor_id\", \"assay_ontology_term_id\", \"suspension_type\")) })  # select features for integration features <- SelectIntegrationFeatures(object.list = seurat_obj.list)  # integrate seurat_obj.list <- PrepSCTIntegration(seurat_obj.list, anchor.features = features) seurat_obj.anchors <- FindIntegrationAnchors(object.list = seurat_obj.list, anchor.features = features, normalization.method = \"SCT\") seurat_obj.combined <- IntegrateData(anchorset = seurat_obj.anchors, normalization.method = \"SCT\")"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the census","title":"Normalizing full-length gene sequencing data","text":"First open Census: can learn cellxgene.census methods accessing corresponding documentation, example ?cellxgene.census::open_soma.","code":"library(\"Seurat\") census <- cellxgene.census::open_soma()"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"fetching-full-length-example-sequencing-data-smart-seq","dir":"Articles","previous_headings":"","what":"Fetching full-length example sequencing data (Smart-Seq)","title":"Normalizing full-length gene sequencing data","text":"Let’s get example data, case ’ll fetch cells relatively small dataset derived Smart-Seq2 technology performs full-length gene sequencing: Collection: Tabula Muris Senis Dataset: Liver - single-cell transcriptomic atlas characterizes ageing tissues mouse - Smart-seq2 Let’s first find dataset’s id using dataset table Census. Now can use id fetch data. Let’s make sure data contains Smart-Seq2 cells. Great! can see small dataset containing 2,859 cells. Now let’s proceed normalize gene lengths.","code":"liver_dataset <- as.data.frame(   census$get(\"census_info\")$get(\"datasets\")         $read(value_filter=\"dataset_title == 'Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2'\")         $concat() ) liver_dataset #>   soma_joinid                        collection_id    collection_name #> 1         525 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #>              collection_doi                           dataset_id #> 1 10.1038/s41586-020-2496-1 4546e757-34d0-4d17-be06-538318925fcd #>                                                                                       dataset_title #> 1 Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2 #>                           dataset_h5ad_path dataset_total_cell_count #> 1 4546e757-34d0-4d17-be06-538318925fcd.h5ad                     2859 liver_dataset_id <- liver_dataset[1,\"dataset_id\"] liver_seurat <- cellxgene.census::get_seurat(   census,   organism = \"Mus musculus\",   obs_value_filter = paste0(\"dataset_id == '\", liver_dataset_id, \"'\") ) table(liver_seurat$assay) #>  #> Smart-seq2  #>       2859"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"normalizing-expression-to-account-for-gene-length","dir":"Articles","previous_headings":"","what":"Normalizing expression to account for gene length","title":"Normalizing full-length gene sequencing data","text":"default cellxgene_census::get_seurat() fetches genes Census. let’s first identify genes measured dataset subset Seurat obect include . goal can use “Dataset Presence Matrix” census$get(\"census_data\")$get(\"mus_musculus\")$ms$get(\"RNA\")$get(\"feature_dataset_presence_matrix\"). boolean matrix N x M N number datasets, M number genes Census, 1 entry indicates gene measured dataset. (Note Seurat objects transposed layout M x N.) Let’s get genes measured dataset. can see genes Census 17,992 measured dataset. Now let’s normalize genes gene length. can easily Census gene lengths included gene metadata feature_length. done! can now see real numbers instead integers.","code":"liver_seurat #> An object of class Seurat  #> 52392 features across 2859 samples within 1 assay  #> Active assay: RNA (52392 features, 0 variable features) liver_dataset_joinid <- liver_dataset$soma_joinid[1] presence_matrix <- cellxgene.census::get_presence_matrix(census, \"Mus musculus\", \"RNA\") presence_matrix <- presence_matrix$take(liver_dataset_joinid) gene_presence <- as.vector(presence_matrix$get_one_based_matrix())  liver_seurat <- liver_seurat[gene_presence, ] liver_seurat #> An object of class Seurat  #> 17992 features across 2859 samples within 1 assay  #> Active assay: RNA (17992 features, 0 variable features) GetAssayData(liver_seurat[1:5, 1:5], slot = \"data\") #> 5 x 5 sparse Matrix of class \"dgCMatrix\" #>                    cell3530079 cell3530080 cell3530081 cell3530082 cell3530083 #> ENSMUSG00000025900           .           .           .           .           . #> ENSMUSG00000025902           .           .           .           .        2250 #> ENSMUSG00000033845           .         559        1969           .           . #> ENSMUSG00000025903           .           .           .           .           . #> ENSMUSG00000033813           .           .         828           1          54 gene_lengths <- liver_seurat$RNA@meta.features$feature_length liver_seurat <- SetAssayData(   liver_seurat,    new.data = sweep(GetAssayData(liver_seurat, slot = \"data\"), 1, gene_lengths, \"/\") ) GetAssayData(liver_seurat[1:5, 1:5], slot = \"data\") #> 5 x 5 sparse Matrix of class \"dgCMatrix\" #>                    cell3530079 cell3530080 cell3530081  cell3530082 cell3530083 #> ENSMUSG00000025900           .  .            .         .             .          #> ENSMUSG00000025902           .  .            .         .             0.47150042 #> ENSMUSG00000033845           .  0.06586544   0.2320019 .             .          #> ENSMUSG00000025903           .  .            .         .             .          #> ENSMUSG00000033813           .  .            0.2744448 0.0003314551  0.01789857"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"validation-through-clustering-exploration","dir":"Articles","previous_headings":"","what":"Validation through clustering exploration","title":"Normalizing full-length gene sequencing data","text":"Let’s perform basic clustering analysis see cell types cluster expected using normalized counts. First basic filtering cells genes. normalize account sequencing depth transform data log scale. subset highly variable genes. finally scale values across gene axis. Now can proceed clustering analysis.  exceptions can see cells cell type cluster near serves sanity check gene-length normalization applied. Don’t forget close census.","code":"cells_per_gene <- rowSums(GetAssayData(liver_seurat, slot = \"counts\") > 0) genes_per_cell <- Matrix::colSums(liver_seurat$RNA@counts > 0) liver_seurat <- liver_seurat[cells_per_gene >= 5, genes_per_cell >= 500] liver_seurat <- Seurat::NormalizeData(   liver_seurat,   normalization.method = \"LogNormalize\",   scale.factor = 10000 ) liver_seurat <- Seurat::FindVariableFeatures(   liver_seurat,   selection.method = \"vst\",   nfeatures = 1000 ) all.genes <- rownames(liver_seurat) liver_seurat <- Seurat::ScaleData(liver_seurat, features = all.genes) liver_seurat <- RunPCA(   liver_seurat,   features = VariableFeatures(object = liver_seurat) ) liver_seurat <- FindNeighbors(liver_seurat, dims = 1:40) liver_seurat <- RunUMAP(liver_seurat, dims = 1:40) DimPlot(liver_seurat, reduction = \"umap\", group.by = \"cell_type\") census$close()"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the Census","title":"Summarizing cell and gene metadata","text":"cellxgene.census R package contains convenient API open version Census (default, newest stable version). open Census, close census$close(). can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma(). can learn cellxgene.census methods accessing corresponding documentation. example ?cellxgene.census::open_soma.","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"summarizing-cell-metadata","dir":"Articles","previous_headings":"","what":"Summarizing cell metadata","title":"Summarizing cell and gene metadata","text":"Census open can use TileDB-SOMA methods SOMACollection. can thus access metadata SOMADataFrame objects encoding cell gene metadata. Tips: can read entire SOMADataFrame R using .data.frame(soma_df$read()$concat()). Queries much faster request DataFrame columns required analysis (e.g. column_names = c(\"soma_joinid\", \"cell_type_ontology_term_id\")). can also refine query results using value_filter, filter census matching records.","code":""},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"example-summarize-all-cell-types","dir":"Articles","previous_headings":"Summarizing cell metadata","what":"Example: Summarize all cell types","title":"Summarizing cell and gene metadata","text":"example reads cell metadata (obs) R data frame summarize variety ways.","code":"human <- census$get(\"census_data\")$get(\"homo_sapiens\")  # Read obs into an R data frame (tibble). obs_df <- human$obs$read(column_names = c(\"cell_type\")) obs_df <- as.data.frame(obs_df$concat())  # Find all unique values in the cell_type column. unique_cell_type <- unique(obs_df$cell_type)  cat(   \"There are\",   length(unique_cell_type),   \"cell types in the Census! The first few are: \",   paste(head(unique_cell_type), collapse = \", \") ) #> There are 613 cell types in the Census! The first few are:  syncytiotrophoblast cell, placental villous trophoblast, extravillous trophoblast, trophoblast giant cell, decidual natural killer cell, human, T cell"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"example-summarize-a-subset-of-cell-types-selected-with-a-value_filter","dir":"Articles","previous_headings":"Summarizing cell metadata","what":"Example: Summarize a subset of cell types, selected with a value_filter","title":"Summarizing cell and gene metadata","text":"example utilizes SOMA “value filter” read subset cells tissue_ontology_term_id equal UBERON:0002048 (lung tissue), summarizes query result. can also define much complex value filters. example: combine terms & | use %% operator query multiple values","code":"# Read cell_type terms for cells which have a specific tissue term LUNG_TISSUE <- \"UBERON:0002048\"  obs_df <- human$obs$read(column_names = c(\"cell_type\"), value_filter = paste0(\"tissue_ontology_term_id == '\", LUNG_TISSUE, \"'\")) obs_df <- as.data.frame(obs_df$concat())  # Find all unique values in the cell_type column as an R data frame. unique_cell_type <- unique(obs_df$cell_type) cat(   \"There are \",   length(unique_cell_type),   \" cell types in the Census where tissue_ontology_term_id == \",   LUNG_TISSUE,   \"!\\nThe first few are:\",   paste(head(unique_cell_type), collapse = \", \"),   \"\\n\" ) #> There are  185  cell types in the Census where tissue_ontology_term_id ==  UBERON:0002048 ! #> The first few are: type II pneumocyte, neutrophil, effector CD4-positive, alpha-beta T cell, effector CD8-positive, alpha-beta T cell, mature NK T cell, blood vessel endothelial cell  # Report the 10 most common top_10 <- sort(table(obs_df$cell_type), decreasing = TRUE)[1:10] cat(   \"The top 10 cell types where tissue_ontology_term_id ==\",    LUNG_TISSUE,   \"are: \",   paste(names(top_10), collapse = \", \") ) #> The top 10 cell types where tissue_ontology_term_id == UBERON:0002048 are:  native cell, alveolar macrophage, CD8-positive, alpha-beta T cell, CD4-positive, alpha-beta T cell, macrophage, type II pneumocyte, classical monocyte, natural killer cell, malignant cell, epithelial cell of lower respiratory tract # You can also do more complex queries, such as testing for inclusion in a list of values obs_df <- human$obs$read(   column_names = c(\"cell_type_ontology_term_id\"),   value_filter = \"tissue_ontology_term_id %in% c('UBERON:0002082', 'UBERON:OOO2084', 'UBERON:0002080')\" )  obs_df <- as.data.frame(obs_df$concat())  # Summarize top_10 <- sort(table(obs_df$cell_type_ontology_term_id), decreasing = TRUE)[1:10] print(top_10) #>  #> CL:0000746 CL:0008034 CL:0002548 CL:0000115 CL:0002131 CL:0000763 CL:0000669 CL:0000003  #>     159096      84750      79618      64190      61830      32088      27515      22707  #> CL:0000057 CL:0002144  #>      20117      18593"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"full-census-metadata-stats","dir":"Articles","previous_headings":"","what":"Full Census metadata stats","title":"Summarizing cell and gene metadata","text":"example queries organisms Census, summarizes diversity various metadata labels.","code":"cols_to_query <- c(   \"cell_type_ontology_term_id\",   \"assay_ontology_term_id\",   \"tissue_ontology_term_id\" )  total_cells <- 0 for (organism in census$get(\"census_data\")$names()) {   print(organism)      obs_df <- census$get(\"census_data\")$get(organism)$obs$read(column_names = cols_to_query)   obs_df <- as.data.frame(obs_df$concat())      total_cells <- total_cells + nrow(obs_df)   for (col in cols_to_query) {     cat(\"  Unique \", col, \" values: \", length(unique(obs_df[[col]])), \"\\n\")   } } #> [1] \"mus_musculus\" #>   Unique  cell_type_ontology_term_id  values:  248  #>   Unique  assay_ontology_term_id  values:  9  #>   Unique  tissue_ontology_term_id  values:  66  #> [1] \"homo_sapiens\" #>   Unique  cell_type_ontology_term_id  values:  613  #>   Unique  assay_ontology_term_id  values:  19  #>   Unique  tissue_ontology_term_id  values:  220 cat(\"Complete Census contains \", total_cells, \" cells.\") #> Complete Census contains  61656118  cells."},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"close-the-census","dir":"Articles","previous_headings":"Full Census metadata stats","what":"Close the census","title":"Summarizing cell and gene metadata","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Chan Zuckerberg Initiative. Author, maintainer, copyright holder, funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Chan Zuckerberg Initiative (2023). cellxgene.census: CZ CELLxGENE Discover Cell Census. R package version 1.7.0, https://github.com/chanzuckerberg/cellxgene-census.","code":"@Manual{,   title = {cellxgene.census: CZ CELLxGENE Discover Cell Census},   author = {{Chan Zuckerberg Initiative}},   year = {2023},   note = {R package version 1.7.0},   url = {https://github.com/chanzuckerberg/cellxgene-census}, }"},{"path":"/index.html","id":"r-package-of-cz-cellxgene-discover-census","dir":"","previous_headings":"","what":"CZ CELLxGENE Discover Cell Census","title":"CZ CELLxGENE Discover Cell Census","text":"documentation R package cellxgene.census part CZ CELLxGENE Discover Census. full details Census data capabilities please go main Census site. cellxgene.census provides API efficiently access cloud-hosted Census single-cell data R. just seconds users can access slice Census data using cell gene filters across hundreds single-cell datasets. Census data can fetched iterative fashion bigger--memory slices data, quickly exported basic R structures, well Seurat SingleCellExperiment objects downstream analysis.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"CZ CELLxGENE Discover Cell Census","text":"installing Ubuntu, may need install following libraries via apt install, libxml2-dev libssl-dev libcurl4-openssl-dev. addition must cmake v3.21 greater. installing MacOS, need install developer tools Xcode. Windows supported. R session install cellxgene.census R-Universe. able export Census data Seurat SingleCellExperiment also need install respective packages.","code":"install.packages(   \"cellxgene.census\",   repos=c('https://chanzuckerberg.r-universe.dev', 'https://cloud.r-project.org') ) # Seurat install.packages(\"Seurat\")  # SingleCellExperiment if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"SingleCellExperiment\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"CZ CELLxGENE Discover Cell Census","text":"Check vignettes “Articles” section navigation bar site. highly recommend following vignettes starting point: Querying fetching single-cell data cell/gene metadata Learning CZ CELLxGENE Discover Census can also check quick start guide main Census site.","code":""},{"path":"/index.html","id":"example-seurat-and-singlecellexperiment-query","dir":"","previous_headings":"Usage","what":"Example Seurat and SingleCellExperiment query","title":"CZ CELLxGENE Discover Cell Census","text":"following creates Seurat object -demand sympathetic neurons Census filtering genes ENSG00000161798, ENSG00000188229. following retrieves data SingleCellExperiment object.","code":"library(\"cellxgene.census\") library(\"Seurat\")  census <- open_soma()  organism <- \"Homo sapiens\" gene_filter <- \"feature_id %in% c('ENSG00000107317', 'ENSG00000106034')\" cell_filter <-  \"cell_type == 'sympathetic neuron'\" cell_columns <- c(\"assay\", \"cell_type\", \"tissue\", \"tissue_general\", \"suspension_type\", \"disease\")  seurat_obj <- get_seurat(    census = census,    organism = organism,    var_value_filter = gene_filter,    obs_value_filter = cell_filter,    obs_column_names = cell_columns ) library(\"SingleCellExperiment\")  sce_obj <- get_single_cell_experiment(    census = census,    organism = organism,    var_value_filter = gene_filter,    obs_value_filter = cell_filter,    obs_column_names = cell_columns )"},{"path":"/index.html","id":"for-more-help","dir":"","previous_headings":"","what":"For More Help","title":"CZ CELLxGENE Discover Cell Census","text":"help, please go visit main Census site. believe found security issue, appreciate notification. Please send email security@chanzuckerberg.com.","code":""},{"path":"/reference/download_source_h5ad.html","id":null,"dir":"Reference","previous_headings":"","what":"Download source H5AD to local file name. — download_source_h5ad","title":"Download source H5AD to local file name. — download_source_h5ad","text":"Download source H5AD local file name.","code":""},{"path":"/reference/download_source_h5ad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download source H5AD to local file name. — download_source_h5ad","text":"","code":"download_source_h5ad(   dataset_id,   file,   overwrite = FALSE,   census_version = \"stable\",   census = NULL )"},{"path":"/reference/download_source_h5ad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download source H5AD to local file name. — download_source_h5ad","text":"dataset_id dataset_id interest. file Local file name store H5AD file. overwrite TRUE allow overwriting existing file. census_version desired Census version. census open Census handle census_version. provided, opened closed automatically; efficient reuse handle calling download_source_h5ad() multiple times.","code":""},{"path":"/reference/download_source_h5ad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download source H5AD to local file name. — download_source_h5ad","text":"","code":"download_source_h5ad(\"0895c838-e550-48a3-a777-dbcd35d30272\", \"/tmp/data.h5ad\", overwrite = TRUE)"},{"path":"/reference/get_census_version_description.html","id":null,"dir":"Reference","previous_headings":"","what":"Get release description for a Census version — get_census_version_description","title":"Get release description for a Census version — get_census_version_description","text":"Get release description Census version","code":""},{"path":"/reference/get_census_version_description.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get release description for a Census version — get_census_version_description","text":"","code":"get_census_version_description(census_version)"},{"path":"/reference/get_census_version_description.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get release description for a Census version — get_census_version_description","text":"census_version census version name.","code":""},{"path":"/reference/get_census_version_description.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get release description for a Census version — get_census_version_description","text":"List release location metadata","code":""},{"path":"/reference/get_census_version_description.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get release description for a Census version — get_census_version_description","text":"","code":"as.data.frame(get_census_version_description(\"stable\")) #>   release_date release_build #> 1                 2023-07-25 #>                                                  soma.uri #> 1 s3://cellxgene-data-public/cell-census/2023-07-25/soma/ #>               soma.relative_uri soma.s3_region #> 1 /cell-census/2023-07-25/soma/      us-west-2 #>                                                  h5ads.uri #> 1 s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/ #>               h5ads.relative_uri h5ads.s3_region do_not_delete  alias #> 1 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE stable #>   census_version #> 1         stable"},{"path":"/reference/get_census_version_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the directory of Census releases currently available — get_census_version_directory","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"Get directory Census releases currently available","code":""},{"path":"/reference/get_census_version_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"","code":"get_census_version_directory()"},{"path":"/reference/get_census_version_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"Data frame available cell census releases, including location metadata.","code":""},{"path":"/reference/get_census_version_directory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"","code":"get_census_version_directory() #>            release_date release_build #> stable                     2023-07-25 #> latest                     2023-10-23 #> 2023-05-15                 2023-05-15 #> 2023-07-25                 2023-07-25 #> 2023-09-25                 2023-09-25 #> 2023-10-03                 2023-10-03 #> 2023-10-09                 2023-10-09 #> 2023-10-18                 2023-10-18 #> 2023-10-23                 2023-10-23 #>                                                           soma.uri #> stable     s3://cellxgene-data-public/cell-census/2023-07-25/soma/ #> latest     s3://cellxgene-data-public/cell-census/2023-10-23/soma/ #> 2023-05-15 s3://cellxgene-data-public/cell-census/2023-05-15/soma/ #> 2023-07-25 s3://cellxgene-data-public/cell-census/2023-07-25/soma/ #> 2023-09-25 s3://cellxgene-data-public/cell-census/2023-09-25/soma/ #> 2023-10-03 s3://cellxgene-data-public/cell-census/2023-10-03/soma/ #> 2023-10-09 s3://cellxgene-data-public/cell-census/2023-10-09/soma/ #> 2023-10-18 s3://cellxgene-data-public/cell-census/2023-10-18/soma/ #> 2023-10-23 s3://cellxgene-data-public/cell-census/2023-10-23/soma/ #>                        soma.relative_uri soma.s3_region #> stable     /cell-census/2023-07-25/soma/      us-west-2 #> latest     /cell-census/2023-10-23/soma/      us-west-2 #> 2023-05-15 /cell-census/2023-05-15/soma/      us-west-2 #> 2023-07-25 /cell-census/2023-07-25/soma/      us-west-2 #> 2023-09-25 /cell-census/2023-09-25/soma/      us-west-2 #> 2023-10-03 /cell-census/2023-10-03/soma/      us-west-2 #> 2023-10-09 /cell-census/2023-10-09/soma/      us-west-2 #> 2023-10-18 /cell-census/2023-10-18/soma/      us-west-2 #> 2023-10-23 /cell-census/2023-10-23/soma/      us-west-2 #>                                                           h5ads.uri #> stable     s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/ #> latest     s3://cellxgene-data-public/cell-census/2023-10-23/h5ads/ #> 2023-05-15 s3://cellxgene-data-public/cell-census/2023-05-15/h5ads/ #> 2023-07-25 s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/ #> 2023-09-25 s3://cellxgene-data-public/cell-census/2023-09-25/h5ads/ #> 2023-10-03 s3://cellxgene-data-public/cell-census/2023-10-03/h5ads/ #> 2023-10-09 s3://cellxgene-data-public/cell-census/2023-10-09/h5ads/ #> 2023-10-18 s3://cellxgene-data-public/cell-census/2023-10-18/h5ads/ #> 2023-10-23 s3://cellxgene-data-public/cell-census/2023-10-23/h5ads/ #>                        h5ads.relative_uri h5ads.s3_region do_not_delete  alias #> stable     /cell-census/2023-07-25/h5ads/       us-west-2          TRUE stable #> latest     /cell-census/2023-10-23/h5ads/       us-west-2          TRUE latest #> 2023-05-15 /cell-census/2023-05-15/h5ads/       us-west-2          TRUE        #> 2023-07-25 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE        #> 2023-09-25 /cell-census/2023-09-25/h5ads/       us-west-2         FALSE        #> 2023-10-03 /cell-census/2023-10-03/h5ads/       us-west-2         FALSE        #> 2023-10-09 /cell-census/2023-10-09/h5ads/       us-west-2         FALSE        #> 2023-10-18 /cell-census/2023-10-18/h5ads/       us-west-2         FALSE        #> 2023-10-23 /cell-census/2023-10-23/h5ads/       us-west-2          TRUE"},{"path":"/reference/get_presence_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Read the feature dataset presence matrix. — get_presence_matrix","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"Read feature dataset presence matrix.","code":""},{"path":"/reference/get_presence_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"","code":"get_presence_matrix(census, organism, measurement_name = \"RNA\")"},{"path":"/reference/get_presence_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"census census object cellxgene.census::open_soma(). organism organism query, usually one Homo sapiens Mus musculus measurement_name measurement object query. Defaults RNA.","code":""},{"path":"/reference/get_presence_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"tiledbsoma::matrixZeroBasedView object dataset join id & feature join id dimensions, filled 1s indicating presence. sparse matrix accessed zero-based indexes since join id's may zero.","code":""},{"path":"/reference/get_presence_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"","code":"census <- open_soma() #> The stable Census release is currently 2023-07-25. Specify census_version = \"2023-07-25\" in future calls to open_soma() to ensure data consistency. on.exit(census$close(), add = TRUE) print(get_presence_matrix(census, \"Homo sapiens\")$dim()) #> Error in private$check_open_for_read_or_write(): Item must be open for read or write. s3://cellxgene-data-public/cell-census/2023-07-25/soma/"},{"path":"/reference/get_seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Census slices to Seurat — get_seurat","title":"Export Census slices to Seurat — get_seurat","text":"Convenience wrapper around SOMAExperimentAxisQuery, build execute query, return Seurat object.","code":""},{"path":"/reference/get_seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Census slices to Seurat — get_seurat","text":"","code":"get_seurat(   census,   organism,   measurement_name = \"RNA\",   X_layers = c(counts = \"raw\", data = NULL),   obs_value_filter = NULL,   obs_coords = NULL,   obs_column_names = NULL,   var_value_filter = NULL,   var_coords = NULL,   var_column_names = NULL,   var_index = \"feature_id\" )"},{"path":"/reference/get_seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Census slices to Seurat — get_seurat","text":"census census object, usually returned cellxgene.census::open_soma(). organism organism query, usually one Homo sapiens Mus musculus measurement_name measurement object query. Defaults RNA. X_layers named character X layers add Seurat assay, names names Seurat slots (counts data) values names layers within X. obs_value_filter SOMA value_filter across columns obs dataframe, expressed string. obs_coords set coordinates obs dataframe index, expressed type format supported SOMADataFrame's read() method. obs_column_names Columns fetch obs data frame. var_value_filter obs_value_filter var. var_coords obs_coords var. var_column_names Columns fetch var data frame. var_index Name column ‘var’ add feature names.","code":""},{"path":"/reference/get_seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export Census slices to Seurat — get_seurat","text":"Seurat object containing sensus slice.","code":""},{"path":"/reference/get_seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Export Census slices to Seurat — get_seurat","text":"","code":"if (FALSE) { census <- open_soma() seurat_obj <- get_seurat(   census,   organism = \"Homo sapiens\",   obs_value_filter = \"cell_type == 'leptomeningeal cell'\",   var_value_filter = \"feature_id %in% c('ENSG00000107317', 'ENSG00000106034')\" )  seurat_obj  census$close() }"},{"path":"/reference/get_single_cell_experiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"Convenience wrapper around SOMAExperimentAxisQuery, build execute query, return SingleCellExperiment object.","code":""},{"path":"/reference/get_single_cell_experiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"","code":"get_single_cell_experiment(   census,   organism,   measurement_name = \"RNA\",   X_layers = c(counts = \"raw\"),   obs_value_filter = NULL,   obs_coords = NULL,   obs_column_names = NULL,   var_value_filter = NULL,   var_coords = NULL,   var_column_names = NULL,   var_index = \"feature_id\" )"},{"path":"/reference/get_single_cell_experiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"census census object, usually returned cellxgene.census::open_soma(). organism organism query, usually one Homo sapiens Mus musculus measurement_name measurement object query. Defaults RNA. X_layers character vector X layers add assays main experiment; may optionally named set name resulting assay (eg. ‘X_layers = c(counts = \"raw\")’ load X layer “‘raw’” assay “‘counts’”); default, loads X layers obs_value_filter SOMA value_filter across columns obs dataframe, expressed string. obs_coords set coordinates obs dataframe index, expressed type format supported SOMADataFrame's read() method. obs_column_names Columns fetch obs data frame. var_value_filter obs_value_filter var. var_coords obs_coords var. var_column_names Columns fetch var data frame. var_index Name column ‘var’ add feature names.","code":""},{"path":"/reference/get_single_cell_experiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"SingleCellExperiment object containing sensus slice.","code":""},{"path":"/reference/get_single_cell_experiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"","code":"if (FALSE) { census <- open_soma() sce_obj <- get_single_cell_experiment(   census,   organism = \"Homo sapiens\",   obs_value_filter = \"cell_type == 'leptomeningeal cell'\",   var_value_filter = \"feature_id %in% c('ENSG00000107317', 'ENSG00000106034')\" )  sce_obj  census$close() }"},{"path":"/reference/get_source_h5ad_uri.html","id":null,"dir":"Reference","previous_headings":"","what":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"Locate source h5ad file dataset.","code":""},{"path":"/reference/get_source_h5ad_uri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"","code":"get_source_h5ad_uri(dataset_id, census_version = \"stable\", census = NULL)"},{"path":"/reference/get_source_h5ad_uri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"dataset_id dataset_id interest. census_version desired Census version. census open Census handle census_version. provided, opened closed automatically; efficient reuse handle calling get_source_h5ad_uri() multiple times.","code":""},{"path":"/reference/get_source_h5ad_uri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"list uri optional s3_region.","code":""},{"path":"/reference/get_source_h5ad_uri.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"","code":"get_source_h5ad_uri(\"0895c838-e550-48a3-a777-dbcd35d30272\") #> $uri #> [1] \"s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/0895c838-e550-48a3-a777-dbcd35d30272.h5ad\" #>  #> $s3_region #> [1] \"us-west-2\" #>"},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":null,"dir":"Reference","previous_headings":"","what":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"Create SOMATileDBContext suitable using open_soma(). Typically open_soma() creates context automatically, one can created separately order set custom configuration options, share multiple open Census handles.","code":""},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"","code":"new_SOMATileDBContext_for_census(census_version_description, ...)"},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"census_version_description result get_census_version_description() desired Census version. ... Custom configuration options.","code":""},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"SOMATileDBContext object open_soma().","code":""},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"","code":"census_desc <- get_census_version_description(\"stable\") ctx <- new_SOMATileDBContext_for_census(census_desc, \"soma.init_buffer_bytes\" = paste(4 * 1024**3)) census <- open_soma(\"stable\", tiledbsoma_ctx = ctx) #> The stable Census release is currently 2023-07-25. Specify census_version = \"2023-07-25\" in future calls to open_soma() to ensure data consistency. census$close()"},{"path":"/reference/open_soma.html","id":null,"dir":"Reference","previous_headings":"","what":"Open the Census — open_soma","title":"Open the Census — open_soma","text":"Open Census","code":""},{"path":"/reference/open_soma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Open the Census — open_soma","text":"","code":"open_soma(census_version = \"stable\", uri = NULL, tiledbsoma_ctx = NULL)"},{"path":"/reference/open_soma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Open the Census — open_soma","text":"census_version version Census, e.g., \"stable\". uri URI containing Census SOMA objects open instead released version. (supplied, takes precedence census_version.) tiledbsoma_ctx tiledbsoma::SOMATileDBContext built using new_SOMATileDBContext_for_census(). Optional (created automatically) using census_version context need reused.","code":""},{"path":"/reference/open_soma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Open the Census — open_soma","text":"Top-level tiledbsoma::SOMACollection object. use, census closed release memory resources, usually .exit(census$close(), add = TRUE). Closing top-level census also close SOMA objects accessed .","code":""},{"path":"/reference/open_soma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Open the Census — open_soma","text":"","code":"census <- open_soma() #> The stable Census release is currently 2023-07-25. Specify census_version = \"2023-07-25\" in future calls to open_soma() to ensure data consistency. as.data.frame(census$get(\"census_info\")$get(\"summary\")$read()$concat()) #>   soma_joinid                      label      value #> 1           0      census_schema_version      1.0.0 #> 2           1          census_build_date 2023-07-25 #> 3           2     dataset_schema_version      3.0.0 #> 4           3           total_cell_count   61656118 #> 5           4          unique_cell_count   37447773 #> 6           5 number_donors_homo_sapiens      13035 #> 7           6 number_donors_mus_musculus       1417 census$close()"}]
+[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 Chan Zuckerberg Initiative Foundation Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/census_compute_over_X.html","id":"incremental-mean-calculation","dir":"Articles","previous_headings":"","what":"Incremental mean calculation","title":"Computing on X using online (incremental) algorithms","text":"Many statistics, marginal means, easy calculate incrementally. Let’s begin query X$raw sparse matrix unnormalized read counts, return results shards incrementally accumulate read count gene, divide cell count get mean reads per cell gene. First define query - case slice obs axis cells specific tissue & sex value, genes var axis. query$X() method returns iterator results, Arrow Table. table contain sparse X data obs/var coordinates, using standard SOMA names: soma_data - X values (float32) soma_dim_0 - obs coordinate (int64) soma_dim_1 - var coordinate (int64) Important: X matrices joined var/obs axis DataFrames integer join “id” (aka soma_joinid). positionally indexed, given cell gene may soma_joinid value (e.g., large integer). words, given X value, soma_dim_0 corresponds soma_joinid obs dataframe, soma_dim_1 coordinate corresponds soma_joinid var dataframe. convenience, query class includes utility simplify operations query slices. query$indexer indexer used wrap output query$X(), converting soma_joinids positional indexing query results. Positions [0, N), N number results query given axis. Key points: expensive query read results - rather make multiple passes data, read perform multiple computations. default, data census indexed soma_joinid positionally.","code":"library(\"tiledbsoma\") library(\"cellxgene.census\") census <- open_soma()  query <- census$get(\"census_data\")$get(\"mus_musculus\")$axis_query(   measurement_name = \"RNA\",   obs_query = SOMAAxisQuery$new(value_filter = \"tissue=='brain' && sex=='male'\") )  genes_df <- query$var(column_names = c(\"feature_id\", \"feature_name\"))$concat() genes_df <- as.data.frame(genes_df) n_genes <- nrow(genes_df)  # accumulator vector (for each gene) for the total count over all cells in X(\"raw\") raw_sum_by_gene <- numeric(n_genes) names(raw_sum_by_gene) <- genes_df$feature_id  # iterate through in-memory shards of query results tables <- query$X(\"raw\")$tables() while (!tables$read_complete()) {   table_part <- tables$read_next()   # table_part is an Arrow table with the columns mentioned above. The result   # order is not guaranteed!    # table_part$soma_dim_1 is the var/gene soma_joinid. But note that these are   # arbitrary int64 id's, and moreover each table_part may exhibit only a subset   # of the values we'll see over all query results. query$indexer helps us map   # any given soma_dim_1 values onto positions in query$var() (genes_df), that is   # the union of all values we'll see.   gene_indexes <- query$indexer$by_var(table_part$soma_dim_1)$as_vector()   stopifnot(sum(gene_indexes >= n_genes) == 0)   # sum(table_part) group by gene, yielding a numeric vector with the gene_index   # in its names   sum_part <- tapply(as.vector(table_part$soma_data), gene_indexes, sum)   # update the accumulator vector   which_genes <- as.integer(names(sum_part)) + 1  # nb: gene_indexes is zero-based   stopifnot(sum(which_genes > n_genes) == 0)   raw_sum_by_gene[which_genes] <- raw_sum_by_gene[which_genes] + sum_part }  # Divide each sum by cell count to get mean reads per cell (for each gene), # implicitly averaging in all zero entries in X even though they weren't included # in the sparse query results. genes_df$raw_mean <- raw_sum_by_gene / query$n_obs genes_df #>            feature_id  feature_name     raw_mean #> 1  ENSMUSG00000051951          Xkr4 1.283861e+00 #> 2  ENSMUSG00000089699        Gm1992 0.000000e+00 #> 3  ENSMUSG00000102343       Gm37381 0.000000e+00 #> 4  ENSMUSG00000025900           Rp1 2.914160e-01 #> 5  ENSMUSG00000025902         Sox17 6.074154e+01 #> 6  ENSMUSG00000104328       Gm37323 5.701742e-05 #> 7  ENSMUSG00000033845        Mrpl15 3.621738e+01 #> 8  ENSMUSG00000025903        Lypla1 1.827366e+01 #> 9  ENSMUSG00000104217       Gm37988 0.000000e+00 #> 10 ENSMUSG00000033813         Tcea1 3.960339e+01 #> 11 ENSMUSG00000002459         Rgs20 3.212989e+00 #> 12 ENSMUSG00000085623       Gm16041 5.701742e-05 #> 13 ENSMUSG00000033793       Atp6v1h 6.866793e+01 #> 14 ENSMUSG00000025905         Oprk1 4.198763e-01 #> 15 ENSMUSG00000033774        Npbwr1 1.140348e-04 #> 16 ENSMUSG00000025907        Rb1cc1 3.342227e+01 #> 17 ENSMUSG00000090031 4732440D04Rik 1.317102e-02 #> 18 ENSMUSG00000087247        Alkal1 5.701742e-05 #> 19 ENSMUSG00000033740          St18 1.525501e+01 #> 20 ENSMUSG00000051285        Pcmtd1 4.981224e+01 #> 21 ENSMUSG00000097797       Gm26901 4.276306e-04 #> 22 ENSMUSG00000103067       Gm30414 0.000000e+00 #> 23 ENSMUSG00000025909         Sntg1 1.083131e+00 #> 24 ENSMUSG00000061024          Rrs1 1.929504e+01 #> 25 ENSMUSG00000025911        Adhfe1 1.163515e+01 #> 26 ENSMUSG00000067879           Vxn 9.911623e+00 #> 27 ENSMUSG00000099827       Gm29520 0.000000e+00 #> 28 ENSMUSG00000025912         Mybl1 2.439775e-01 #> 29 ENSMUSG00000045210        Vcpip1 3.177732e+01 #> 30 ENSMUSG00000097893 1700034P13Rik 5.257291e-01 #> 31 ENSMUSG00000025915          Sgk3 1.850455e+01 #> 32 ENSMUSG00000046101        Mcmdc2 6.555578e-01 #> 33 ENSMUSG00000098234         Snhg6 6.245488e+00 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 52359 rows ]"},{"path":"/articles/census_compute_over_X.html","id":"counting-cells-grouped-by-dataset-and-gene","dir":"Articles","previous_headings":"","what":"Counting cells grouped by dataset and gene","title":"Computing on X using online (incremental) algorithms","text":"goal example count number cells nonzero reads, grouped gene Census dataset_id. result data frame dataset, gene, number cells nonzero reads dataset gene. multi-factor aggregation, ’ll take advantage dplyr routines instead lower-level vector indexer shown . presentation purposes, ’ll limit query four genes, can expanded genes easily. Don’t forget close census.","code":"library(\"dplyr\")  query <- census$get(\"census_data\")$get(\"mus_musculus\")$axis_query(   measurement_name = \"RNA\",   obs_query = SOMAAxisQuery$new(value_filter = \"tissue=='brain'\"),   var_query = SOMAAxisQuery$new(value_filter = \"feature_name %in% c('Malat1', 'Ptprd', 'Dlg2', 'Pcdh9')\") )  obs_tbl <- query$obs(column_names=c(\"soma_joinid\", \"dataset_id\"))$concat() obs_df <- data.frame(   # materialize soma_joinid as character to avoid overflowing R 32-bit integer   cell_id = as.character(obs_tbl$soma_joinid),   dataset_id = obs_tbl$dataset_id$as_vector() ) var_tbl <- query$var(column_names=c(\"soma_joinid\", \"feature_name\"))$concat() var_df <- data.frame(   gene_id = as.character(var_tbl$soma_joinid),   feature_name = var_tbl$feature_name$as_vector() )  # accumulator for # cells by dataset & gene n_cells_grouped <- data.frame(   \"dataset_id\" = character(0),   \"gene_id\" = character(0),   \"n_cells\" = numeric(0) )  # iterate through in-memory shards of query results tables <- query$X(\"raw\")$tables() while (!tables$read_complete()) {   table_part <- tables$read_next()    # prepare a (dataset,gene,1) tuple for each entry in table_part   n_cells_part <- data.frame(     \"cell_id\" = as.character(table_part$soma_dim_0),     \"gene_id\" = as.character(table_part$soma_dim_1),     \"n_cells\" = 1   )   n_cells_part <- left_join(n_cells_part, obs_df, by = \"cell_id\")   stopifnot(sum(is.null(n_cells_part$dataset_id)) == 0)    # fold those into n_cells_grouped   n_cells_grouped <- n_cells_part %>%                      select(-cell_id) %>%                      bind_rows(n_cells_grouped) %>%                       group_by(dataset_id, gene_id) %>%                       summarise(n_cells = sum(n_cells)) %>%                       ungroup() }  # add gene names for display n_cells_grouped <- left_join(n_cells_grouped, var_df, by = \"gene_id\") stopifnot(sum(is.null(n_cells_grouped$feature_name)) == 0) n_cells_grouped[c(\"dataset_id\", \"feature_name\", \"n_cells\")] #> # A tibble: 17 × 3 #>    dataset_id                           feature_name n_cells #>    <chr>                                <chr>          <dbl> #>  1 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Dlg2           79513 #>  2 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Pcdh9          79476 #>  3 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Malat1         79667 #>  4 3bbb6cf9-72b9-41be-b568-656de6eb18b5 Ptprd          79578 #>  5 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Dlg2              81 #>  6 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Pcdh9            125 #>  7 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Malat1         12622 #>  8 58b01044-c5e5-4b0f-8a2d-6ebf951e01ff Ptprd            474 #>  9 66ff82b4-9380-469c-bc4b-cfa08eacd325 Dlg2             856 #> 10 66ff82b4-9380-469c-bc4b-cfa08eacd325 Pcdh9           2910 #> 11 66ff82b4-9380-469c-bc4b-cfa08eacd325 Malat1          7102 #> 12 98e5ea9f-16d6-47ec-a529-686e76515e39 Dlg2             908 #> 13 98e5ea9f-16d6-47ec-a529-686e76515e39 Pcdh9           3027 #> 14 98e5ea9f-16d6-47ec-a529-686e76515e39 Malat1         20094 #> 15 c08f8441-4a10-4748-872a-e70c0bcccdba Dlg2              52 #> 16 c08f8441-4a10-4748-872a-e70c0bcccdba Pcdh9            117 #> 17 c08f8441-4a10-4748-872a-e70c0bcccdba Malat1         12992 census$close()"},{"path":"/articles/census_dataset_presence.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the Census","title":"Genes measured in each cell (dataset presence matrix)","text":"cellxgene.census R package contains convenient API open version Census (default, newest stable version).","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/census_dataset_presence.html","id":"fetching-the-ids-of-the-census-datasets","dir":"Articles","previous_headings":"","what":"Fetching the IDs of the Census datasets","title":"Genes measured in each cell (dataset presence matrix)","text":"Let’s grab table datasets included Census use table combination presence matrix .","code":"# Grab the experiment containing human data, and the measurement therein with RNA human <- census$get(\"census_data\")$get(\"homo_sapiens\") human_rna <- human$ms$get(\"RNA\")  # The census-wide datasets datasets_df <- as.data.frame(census$get(\"census_info\")$get(\"datasets\")$read()$concat()) print(datasets_df) #>    soma_joinid                        collection_id #> 1            0 e2c257e7-6f79-487c-b81c-39451cd4ab3c #> 2            1 e2c257e7-6f79-487c-b81c-39451cd4ab3c #> 3            2 e2c257e7-6f79-487c-b81c-39451cd4ab3c #> 4            3 f7cecffa-00b4-4560-a29a-8ad626b8ee08 #> 5            4 3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c #> 6            5 e5f58829-1a66-40b5-a624-9046778e74f5 #> 7            6 e5f58829-1a66-40b5-a624-9046778e74f5 #> 8            7 e5f58829-1a66-40b5-a624-9046778e74f5 #> 9            8 e5f58829-1a66-40b5-a624-9046778e74f5 #> 10           9 e5f58829-1a66-40b5-a624-9046778e74f5 #> 11          10 e5f58829-1a66-40b5-a624-9046778e74f5 #> 12          11 e5f58829-1a66-40b5-a624-9046778e74f5 #>                                                                                                                             collection_name #> 1                                                                      Spatial multiomics map of trophoblast development in early pregnancy #> 2                                                                      Spatial multiomics map of trophoblast development in early pregnancy #> 3                                                                      Spatial multiomics map of trophoblast development in early pregnancy #> 4                                       Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                            Tabula Sapiens #> 7                                                                                                                            Tabula Sapiens #> 8                                                                                                                            Tabula Sapiens #> 9                                                                                                                            Tabula Sapiens #> 10                                                                                                                           Tabula Sapiens #> 11                                                                                                                           Tabula Sapiens #> 12                                                                                                                           Tabula Sapiens #>                 collection_doi                           dataset_id #> 1   10.1038/s41586-023-05869-0 f171db61-e57e-4535-a06a-35d8b6ef8f2b #> 2   10.1038/s41586-023-05869-0 ecf2e08e-2032-4a9e-b466-b65b395f4a02 #> 3   10.1038/s41586-023-05869-0 74cff64f-9da9-4b2a-9b3b-8a04a1598040 #> 4  10.1016/j.ccell.2022.11.001 5af90777-6760-4003-9dba-8f945fec6fdf #> 5  10.1016/j.ccell.2021.03.007 bd65a70f-b274-4133-b9dd-0d1431b6af34 #> 6      10.1126/science.abl4896 ff45e623-7f5f-46e3-b47d-56be0341f66b #> 7      10.1126/science.abl4896 f01bdd17-4902-40f5-86e3-240d66dd2587 #> 8      10.1126/science.abl4896 e6a11140-2545-46bc-929e-da243eed2cae #> 9      10.1126/science.abl4896 e5c63d94-593c-4338-a489-e1048599e751 #> 10     10.1126/science.abl4896 d8732da6-8d1d-42d9-b625-f2416c30054b #> 11     10.1126/science.abl4896 d77ec7d6-ef2e-49d6-9e79-05b7f8881484 #> 12     10.1126/science.abl4896 cee11228-9f0b-4e57-afe2-cfe15ee56312 #>                                                                                                                               dataset_title #> 1                                                                                                                    donor_p13_trophoblasts #> 2                                                                                                                   All donors trophoblasts #> 3                                                                                                      All donors all cell states (in vivo) #> 4                                                                      Single-cell transcriptomic datasets of Renal cell carcinoma patients #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                 Tabula Sapiens - Pancreas #> 7                                                                                                           Tabula Sapiens - Salivary_Gland #> 8                                                                                                                    Tabula Sapiens - Heart #> 9                                                                                                                  Tabula Sapiens - Bladder #> 10                                                                                                                 Tabula Sapiens - Trachea #> 11                                                                                                                Tabula Sapiens - Prostate #> 12                                                                                                                  Tabula Sapiens - Spleen #>                            dataset_h5ad_path dataset_total_cell_count #> 1  f171db61-e57e-4535-a06a-35d8b6ef8f2b.h5ad                    31497 #> 2  ecf2e08e-2032-4a9e-b466-b65b395f4a02.h5ad                    67070 #> 3  74cff64f-9da9-4b2a-9b3b-8a04a1598040.h5ad                   286326 #> 4  5af90777-6760-4003-9dba-8f945fec6fdf.h5ad                   270855 #> 5  bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad                   167283 #> 6  ff45e623-7f5f-46e3-b47d-56be0341f66b.h5ad                    13497 #> 7  f01bdd17-4902-40f5-86e3-240d66dd2587.h5ad                    27199 #> 8  e6a11140-2545-46bc-929e-da243eed2cae.h5ad                    11505 #> 9  e5c63d94-593c-4338-a489-e1048599e751.h5ad                    24583 #> 10 d8732da6-8d1d-42d9-b625-f2416c30054b.h5ad                     9522 #> 11 d77ec7d6-ef2e-49d6-9e79-05b7f8881484.h5ad                    16375 #> 12 cee11228-9f0b-4e57-afe2-cfe15ee56312.h5ad                    34004 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 581 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"fetching-the-dataset-presence-matrix","dir":"Articles","previous_headings":"","what":"Fetching the dataset presence matrix","title":"Genes measured in each cell (dataset presence matrix)","text":"Now let’s fetch dataset presence matrix. convenience, read entire presence matrix (Homo sapiens) sparse matrix. convenience function providing capability: also need var dataframe, read R data frame convenient manipulation:","code":"presence_matrix <- get_presence_matrix(census, \"Homo sapiens\", \"RNA\") print(dim(presence_matrix)) #> NULL var_df <- as.data.frame(human_rna$var$read()$concat()) print(var_df) #>    soma_joinid      feature_id feature_name feature_length #> 1            0 ENSG00000121410         A1BG           3999 #> 2            1 ENSG00000268895     A1BG-AS1           3374 #> 3            2 ENSG00000148584         A1CF           9603 #> 4            3 ENSG00000175899          A2M           6318 #> 5            4 ENSG00000245105      A2M-AS1           2948 #> 6            5 ENSG00000166535        A2ML1           7156 #> 7            6 ENSG00000256661    A2ML1-AS1            452 #> 8            7 ENSG00000184389      A3GALT2           1023 #> 9            8 ENSG00000128274       A4GALT           3358 #> 10           9 ENSG00000118017        A4GNT           1779 #> 11          10 ENSG00000094914         AAAS           4727 #> 12          11 ENSG00000081760         AACS          16039 #> 13          12 ENSG00000114771        AADAC           1632 #> 14          13 ENSG00000197953      AADACL2           5071 #> 15          14 ENSG00000242908  AADACL2-AS1            635 #> 16          15 ENSG00000188984      AADACL3           4055 #> 17          16 ENSG00000204518      AADACL4           2027 #> 18          17 ENSG00000109576        AADAT           2970 #> 19          18 ENSG00000158122       PRXL2C           3098 #> 20          19 ENSG00000103591        AAGAB           4138 #> 21          20 ENSG00000115977         AAK1          24843 #> 22          21 ENSG00000087884        AAMDC           1943 #> 23          22 ENSG00000127837         AAMP           3055 #> 24          23 ENSG00000129673        AANAT           2156 #> 25          24 ENSG00000131043         AAR2           7291 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 60639 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"identifying-genes-measured-in-a-specific-dataset","dir":"Articles","previous_headings":"","what":"Identifying genes measured in a specific dataset","title":"Genes measured in each cell (dataset presence matrix)","text":"Now dataset table, genes metadata table, dataset presence matrix, can check gene set genes measured specific dataset. Important: presence matrix indexed soma_joinid, positionally indexed. words: first dimension presence matrix dataset’s soma_joinid, stored census_datasets dataframe. second dimension presence matrix feature’s soma_joinid, stored var dataframe. presence matrix method $take() lets slice soma_joinids census_datasets var. full presence matrix, slices , can exported regular matrix method $get_one_based_matrix() Let’s find gene \"ENSG00000286096\" measured dataset id \"97a17473-e2b1-4f31-a544-44a60773e2dd\".","code":"# Get soma_joinid for datasets and genes of interest var_joinid <- var_df$soma_joinid[var_df$feature_id == \"ENSG00000286096\"] dataset_joinid <- datasets_df$soma_joinid[datasets_df$dataset_id == \"97a17473-e2b1-4f31-a544-44a60773e2dd\"]  # Slice presence matrix with datasets and genes of interest presence_matrix_slice <- presence_matrix$take(i = dataset_joinid, j = var_joinid)  # Convert presence matrix to regular matrix presence_matrix_slice <- presence_matrix_slice$get_one_based_matrix()  # Find how if the gene is present in this dataset is_present <- presence_matrix_slice[, , drop = TRUE] cat(paste(\"Feature is\", if (is_present) \"present.\" else \"not present.\")) #> Feature is present."},{"path":"/articles/census_dataset_presence.html","id":"identifying-datasets-that-measured-specific-genes","dir":"Articles","previous_headings":"","what":"Identifying datasets that measured specific genes","title":"Genes measured in each cell (dataset presence matrix)","text":"Similarly, can determine datasets measured specific gene set genes.","code":"# Grab the feature's soma_joinid from the var dataframe var_joinid <- var_df$soma_joinid[var_df$feature_id == \"ENSG00000286096\"]  # The presence matrix is indexed by the joinids of the dataset and var dataframes, # so slice out the feature of interest by its joinid. presence_matrix_slice  <- presence_matrix$take(j = var_joinid)$get_one_based_matrix() measured_datasets <- presence_matrix_slice[, , drop = TRUE] != 0 dataset_joinids <- datasets_df$soma_joinid[measured_datasets]  # From the datasets dataframe, slice out the datasets which have a joinid in the list print(datasets_df[dataset_joinids, ]) #>    soma_joinid                        collection_id #> 5            4 3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c #> 6            5 e5f58829-1a66-40b5-a624-9046778e74f5 #> 7            6 e5f58829-1a66-40b5-a624-9046778e74f5 #> 8            7 e5f58829-1a66-40b5-a624-9046778e74f5 #> 9            8 e5f58829-1a66-40b5-a624-9046778e74f5 #> 11          10 e5f58829-1a66-40b5-a624-9046778e74f5 #> 12          11 e5f58829-1a66-40b5-a624-9046778e74f5 #> 14          13 e5f58829-1a66-40b5-a624-9046778e74f5 #> 15          14 e5f58829-1a66-40b5-a624-9046778e74f5 #> 19          18 e5f58829-1a66-40b5-a624-9046778e74f5 #> 20          19 e5f58829-1a66-40b5-a624-9046778e74f5 #> 21          20 e5f58829-1a66-40b5-a624-9046778e74f5 #>                                                                                                                             collection_name #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                            Tabula Sapiens #> 7                                                                                                                            Tabula Sapiens #> 8                                                                                                                            Tabula Sapiens #> 9                                                                                                                            Tabula Sapiens #> 11                                                                                                                           Tabula Sapiens #> 12                                                                                                                           Tabula Sapiens #> 14                                                                                                                           Tabula Sapiens #> 15                                                                                                                           Tabula Sapiens #> 19                                                                                                                           Tabula Sapiens #> 20                                                                                                                           Tabula Sapiens #> 21                                                                                                                           Tabula Sapiens #>                 collection_doi                           dataset_id #> 5  10.1016/j.ccell.2021.03.007 bd65a70f-b274-4133-b9dd-0d1431b6af34 #> 6      10.1126/science.abl4896 ff45e623-7f5f-46e3-b47d-56be0341f66b #> 7      10.1126/science.abl4896 f01bdd17-4902-40f5-86e3-240d66dd2587 #> 8      10.1126/science.abl4896 e6a11140-2545-46bc-929e-da243eed2cae #> 9      10.1126/science.abl4896 e5c63d94-593c-4338-a489-e1048599e751 #> 11     10.1126/science.abl4896 d77ec7d6-ef2e-49d6-9e79-05b7f8881484 #> 12     10.1126/science.abl4896 cee11228-9f0b-4e57-afe2-cfe15ee56312 #> 14     10.1126/science.abl4896 a2d4d33e-4c62-4361-b80a-9be53d2e50e8 #> 15     10.1126/science.abl4896 a0754256-f44b-4c4a-962c-a552e47d3fdc #> 19     10.1126/science.abl4896 6d41668c-168c-4500-b06a-4674ccf3e19d #> 20     10.1126/science.abl4896 5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c #> 21     10.1126/science.abl4896 55cf0ea3-9d2b-4294-871e-bb4b49a79fc7 #>                                                                                                                               dataset_title #> 5  Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                 Tabula Sapiens - Pancreas #> 7                                                                                                           Tabula Sapiens - Salivary_Gland #> 8                                                                                                                    Tabula Sapiens - Heart #> 9                                                                                                                  Tabula Sapiens - Bladder #> 11                                                                                                                Tabula Sapiens - Prostate #> 12                                                                                                                  Tabula Sapiens - Spleen #> 14                                                                                                             Tabula Sapiens - Vasculature #> 15                                                                                                                     Tabula Sapiens - Eye #> 19                                                                                                                   Tabula Sapiens - Liver #> 20                                                                                                                     Tabula Sapiens - Fat #> 21                                                                                                                  Tabula Sapiens - Tongue #>                            dataset_h5ad_path dataset_total_cell_count #> 5  bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad                   167283 #> 6  ff45e623-7f5f-46e3-b47d-56be0341f66b.h5ad                    13497 #> 7  f01bdd17-4902-40f5-86e3-240d66dd2587.h5ad                    27199 #> 8  e6a11140-2545-46bc-929e-da243eed2cae.h5ad                    11505 #> 9  e5c63d94-593c-4338-a489-e1048599e751.h5ad                    24583 #> 11 d77ec7d6-ef2e-49d6-9e79-05b7f8881484.h5ad                    16375 #> 12 cee11228-9f0b-4e57-afe2-cfe15ee56312.h5ad                    34004 #> 14 a2d4d33e-4c62-4361-b80a-9be53d2e50e8.h5ad                    16037 #> 15 a0754256-f44b-4c4a-962c-a552e47d3fdc.h5ad                    10650 #> 19 6d41668c-168c-4500-b06a-4674ccf3e19d.h5ad                     5007 #> 20 5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c.h5ad                    20263 #> 21 55cf0ea3-9d2b-4294-871e-bb4b49a79fc7.h5ad                    15020 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 30 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"identifying-all-genes-measured-in-a-dataset","dir":"Articles","previous_headings":"","what":"Identifying all genes measured in a dataset","title":"Genes measured in each cell (dataset presence matrix)","text":"Finally, can find set genes measured cells given dataset.","code":"# Slice the dataset(s) of interest, and get the joinid(s) dataset_joinids <- datasets_df$soma_joinid[datasets_df$collection_id == \"17481d16-ee44-49e5-bcf0-28c0780d8c4a\"]  # Slice the presence matrix by the first dimension, i.e., by dataset presence_matrix_slice <- presence_matrix$take(i = dataset_joinids)$get_one_based_matrix() genes_measured <- Matrix::colSums(presence_matrix_slice) > 0 var_joinids <- var_df$soma_joinid[genes_measured]  print(var_df[var_joinids, ]) #>    soma_joinid      feature_id feature_name feature_length #> 1            0 ENSG00000121410         A1BG           3999 #> 2            1 ENSG00000268895     A1BG-AS1           3374 #> 3            2 ENSG00000148584         A1CF           9603 #> 4            3 ENSG00000175899          A2M           6318 #> 5            4 ENSG00000245105      A2M-AS1           2948 #> 6            5 ENSG00000166535        A2ML1           7156 #> 8            7 ENSG00000184389      A3GALT2           1023 #> 9            8 ENSG00000128274       A4GALT           3358 #> 10           9 ENSG00000118017        A4GNT           1779 #> 11          10 ENSG00000094914         AAAS           4727 #> 12          11 ENSG00000081760         AACS          16039 #> 13          12 ENSG00000114771        AADAC           1632 #> 14          13 ENSG00000197953      AADACL2           5071 #> 16          15 ENSG00000188984      AADACL3           4055 #> 17          16 ENSG00000204518      AADACL4           2027 #> 18          17 ENSG00000109576        AADAT           2970 #> 19          18 ENSG00000158122       PRXL2C           3098 #> 20          19 ENSG00000103591        AAGAB           4138 #> 21          20 ENSG00000115977         AAK1          24843 #> 22          21 ENSG00000087884        AAMDC           1943 #> 23          22 ENSG00000127837         AAMP           3055 #> 24          23 ENSG00000129673        AANAT           2156 #> 25          24 ENSG00000131043         AAR2           7291 #> 26          25 ENSG00000205002         AARD           2404 #> 27          26 ENSG00000090861        AARS1          10248 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 27185 rows ]"},{"path":"/articles/census_dataset_presence.html","id":"close-the-census","dir":"Articles","previous_headings":"Identifying all genes measured in a dataset","what":"Close the census","title":"Genes measured in each cell (dataset presence matrix)","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/articles/census_datasets.html","id":"fetching-the-datasets-table","dir":"Articles","previous_headings":"","what":"Fetching the datasets table","title":"Census Datasets example","text":"Census contains top-level data frame itemizing datasets contained therein. can read SOMADataFrame Arrow Table: R data frame: sum cell counts across datasets match number cells across SOMA experiments (human, mouse).","code":"library(\"cellxgene.census\") census <- open_soma() census_datasets <- census$get(\"census_info\")$get(\"datasets\")$read()$concat() print(census_datasets) #> Table #> 593 rows x 8 columns #> $soma_joinid <int64 not null> #> $collection_id <large_string not null> #> $collection_name <large_string not null> #> $collection_doi <large_string not null> #> $dataset_id <large_string not null> #> $dataset_title <large_string not null> #> $dataset_h5ad_path <large_string not null> #> $dataset_total_cell_count <int64 not null> census_datasets <- as.data.frame(census_datasets) print(census_datasets[, c(   \"dataset_id\",   \"dataset_title\",   \"dataset_total_cell_count\" )]) #>                              dataset_id #> 1  f171db61-e57e-4535-a06a-35d8b6ef8f2b #> 2  ecf2e08e-2032-4a9e-b466-b65b395f4a02 #> 3  74cff64f-9da9-4b2a-9b3b-8a04a1598040 #> 4  5af90777-6760-4003-9dba-8f945fec6fdf #> 5  bd65a70f-b274-4133-b9dd-0d1431b6af34 #> 6  ff45e623-7f5f-46e3-b47d-56be0341f66b #> 7  f01bdd17-4902-40f5-86e3-240d66dd2587 #> 8  e6a11140-2545-46bc-929e-da243eed2cae #> 9  e5c63d94-593c-4338-a489-e1048599e751 #> 10 d8732da6-8d1d-42d9-b625-f2416c30054b #> 11 d77ec7d6-ef2e-49d6-9e79-05b7f8881484 #> 12 cee11228-9f0b-4e57-afe2-cfe15ee56312 #> 13 a357414d-2042-4eb5-95f0-c58604a18bdd #> 14 a2d4d33e-4c62-4361-b80a-9be53d2e50e8 #> 15 a0754256-f44b-4c4a-962c-a552e47d3fdc #> 16 983d5ec9-40e8-4512-9e65-a572a9c486cb #> 17 7357cee7-9f7f-4ab0-8cec-90de8f047e38 #> 18 6ec405bb-4727-4c6d-ab4e-01fe489af7ea #> 19 6d41668c-168c-4500-b06a-4674ccf3e19d #> 20 5e5e7a2f-8f1c-42ac-90dc-b4f80f38e84c #> 21 55cf0ea3-9d2b-4294-871e-bb4b49a79fc7 #> 22 4f1555bc-4664-46c3-a606-78d34dd10d92 #> 23 2ba40233-8576-4dec-a5f1-2adfa115e2dc #> 24 2423ce2c-3149-4cca-a2ff-cf682ea29b5f #> 25 1c9eb291-6d31-47e1-96b2-129b5e1ae64f #> 26 18eb630b-a754-4111-8cd4-c24ec80aa5ec #> 27 0d2ee4ac-05ee-40b2-afb6-ebb584caa867 #> 28 0ced5e76-6040-47ff-8a72-93847965afc0 #> 29 0041b9c3-6a49-4bf7-8514-9bc7190067a7 #> 30 c8f83821-a242-4ed7-86e9-7da077f5d348 #> 31 c05e6940-729c-47bd-a2a6-6ce3730c4919 #> 32 894573ad-498f-47ee-9bec-ad0880147eea #> 33 84242d25-f656-4ca6-8e8d-f3d2beeba11f #> 34 3d044b52-140a-4528-bf0d-a2dbef9e1f40 #> 35 12194ced-8086-458e-84a8-e2ab935d8db1 #> 36 965386e9-1e4f-466d-bf59-ebdca4b66b9b #> 37 e47c65a8-7d2f-48b8-908e-04ea6505fa26 #> 38 c8d40d53-387b-48f2-9f89-72bfdb9c7c9f #> 39 a41202e6-173c-477c-8b4d-e0688ee1c4cb #> 40 7f08cbcc-5790-4576-8478-10e9a999b316 #> 41 3d0d6923-a2c4-476f-a53e-3b7bfa010d90 #> 42 2a7f90de-098a-4a6f-ac07-9e6462792412 #> 43 d7291f04-fbbb-4d65-990a-f01fa44e915b #> 44 8e10f1c4-8e98-41e5-b65f-8cd89a887122 #> 45 9d8e5dca-03a3-457d-b7fb-844c75735c83 #> 46 a6388a6f-6076-401b-9b30-7d4306a20035 #> 47 842c6f5d-4a94-4eef-8510-8c792d1124bc #> 48 74520626-b0ba-4ee9-86b5-714649554def #> 49 396a9124-fb20-4822-bf9c-e93fdf7c999a #> 50 e84f2780-51e8-4cfa-8aa0-13bbfef677c7 #> 51 dfdf1ae2-d624-4004-9353-f18b902f6bca #> 52 d1cbed97-d88f-4954-8925-13302fe30b39 #> 53 b03e4ef8-4e6b-47f4-84a7-e8ed033d08cd #> 54 f1f123cc-ca2c-460f-b7f1-88240efb1e82 #> 55 de94c504-4b58-4f42-b68d-74a8e4892f0e #> 56 da684768-fb01-455b-9f0f-b63a3e2f844f #> 57 9f222629-9e39-47d0-b83f-e08d610c7479 #> 58 b0e547f0-462b-4f81-b31b-5b0a5d96f537 #> 59 d41f45c1-1b7b-4573-a998-ac5c5acb1647 #> 60 f801b7a9-80a6-4d09-9161-71474deb58ae #> 61 be39785b-67cb-4177-be19-a40ee3747e45 #> 62 4c6f9f26-5470-455b-8933-c408232fbf56 #> 63 be35c935-ee4f-475c-9d3c-97630d59a735 #> 64 975e13b6-bec1-4eed-b46a-9be1f1357373 #> 65 24ec2dc5-3573-4d66-a9e1-25b7dcf43e27 #> 66 0ba636a1-4754-4786-a8be-7ab3cf760fd6 #> 67 c2876b1b-06d8-4d96-a56b-5304f815b99a #> 68 3f32121d-126b-4e8d-9f69-d86502d2a1b1 #> 69 be46dfdc-0f99-4731-8957-64ca37364985 #> 70 ac2fea99-ce08-4fca-8d03-a19f37bf21a3 #> 71 a13bda79-9134-46c9-9ed1-a2858be9aafe #> 72 5695d556-974e-4d92-9e99-5f61b8695313 #> 73 535e9336-2d8d-43c3-944d-bcbebe20df8a #> 74 4fb330ab-2d74-4649-b58f-7ffef457efdf #> 75 290d50c7-7158-4198-acf5-6d4b624fd3dc #> 76 18e2a8c5-33f7-455e-a58a-b2ba6921db27 #> 77 12967895-3d58-4e93-be2c-4e1bcf4388d5 #> 78 ee195b7d-184d-4dfa-9b1c-51a7e601ac11 #> 79 9968be68-ab65-4a38-9e1a-c9b6abece194 #> 80 b165f033-9dec-468a-9248-802fc6902a74 #> 81 d8da613f-e681-4c69-b463-e94f5e66847f #> 82 fe4b89d5-461e-440c-a5a8-621b37b122c0 #> 83 a37f857c-779f-464e-9310-3db43a1811e7 #> 84 6cf3634d-e911-44ad-bf52-c747a9af3c01 #> 85 5ce42b38-d867-487f-9b40-e8bb00b21d0b #>                                                                                                                                     dataset_title #> 1                                                                                                                          donor_p13_trophoblasts #> 2                                                                                                                         All donors trophoblasts #> 3                                                                                                            All donors all cell states (in vivo) #> 4                                                                            Single-cell transcriptomic datasets of Renal cell carcinoma patients #> 5        Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy #> 6                                                                                                                       Tabula Sapiens - Pancreas #> 7                                                                                                                 Tabula Sapiens - Salivary_Gland #> 8                                                                                                                          Tabula Sapiens - Heart #> 9                                                                                                                        Tabula Sapiens - Bladder #> 10                                                                                                                       Tabula Sapiens - Trachea #> 11                                                                                                                      Tabula Sapiens - Prostate #> 12                                                                                                                        Tabula Sapiens - Spleen #> 13                                                                                                               Tabula Sapiens - Small_Intestine #> 14                                                                                                                   Tabula Sapiens - Vasculature #> 15                                                                                                                           Tabula Sapiens - Eye #> 16                                                                                                                         Tabula Sapiens - Blood #> 17                                                                                                               Tabula Sapiens - Large_Intestine #> 18                                                                                                                        Tabula Sapiens - Uterus #> 19                                                                                                                         Tabula Sapiens - Liver #> 20                                                                                                                           Tabula Sapiens - Fat #> 21                                                                                                                        Tabula Sapiens - Tongue #> 22                                                                                                                   Tabula Sapiens - Bone_Marrow #> 23                                                                                                                       Tabula Sapiens - Mammary #> 24                                                                                                                        Tabula Sapiens - Kidney #> 25                                                                                                                        Tabula Sapiens - Muscle #> 26                                                                                                                    Tabula Sapiens - Lymph_Node #> 27                                                                                                                          Tabula Sapiens - Lung #> 28                                                                                                                        Tabula Sapiens - Thymus #> 29                                                                                                                          Tabula Sapiens - Skin #> 30                                                                                                                      white matter - astrocytes #> 31                                                                                                                       white matter - all cells #> 32                                                                                                                         white matter - neurons #> 33                                                                                                                       white matter - microglia #> 34                                                                                                                  white matter - vascular cells #> 35                                                                                                                 white matter - oligodendroglia #> 36 Single cell RNA sequencing of bone marrow mononuclear cells from healthy donors and B-cell lymphoma patients following CD19 CAR T-cell therapy #> 37                                                                                                                scRNA-seq data - vascular cells #> 38                                                                                                            scRNA-seq data - perivascular cells #> 39                                                                                                               scRNA-seq data - lymphatic cells #> 40                                                                                                                     snRNA-seq data - all cells #> 41                                                                                                                   scRNA-seq data - fibroblasts #> 42                                                                                                              scRNA-seq data - epithelial cells #> 43                                                                               Single-cell RNA-seq for all cortical & hippocampal regions (10x) #> 44                                                                                                                                    All neurons #> 45                                                                                                                          22 integrated samples #> 46                                                                                                                 scRNA-seq data - myeloid cells #> 47                                                                                                                     scRNA-seq data - all cells #> 48                                                                                                                       scRNA-seq data - T cells #> 49                                                                                                                       scRNA-seq data - B cells #> 50                                                                                                             All cells from human liver dataset #> 51                                                                                                         Myeloid cells from human liver dataset #> 52                                                                                                   CD45 negative cells from human liver dataset #> 53                                                                                                        Lymphoid cells from human liver dataset #> 54                                                                                                 UMAP visualization of keratinocyte subclusters #> 55                                                                                                          UMAP visualization of all 12 datasets #> 56                                                                                                   UMAP visualization of fibroblast subclusters #> 57                                                                        An integrated cell atlas of the human lung in health and disease (full) #> 58                                         AIDA Phase 1: Chinese, Indian, Japanese, Korean, and Malay donors in Japan, Singapore, and South Korea #> 59           Transcriptional connectivity of regulatory T cells in the tumor microenvironment informs novel combination cancer therapy strategies #> 60          normal - Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response #> 61           ccRCC - Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response #> 62           chRCC - Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response #> 63                                                                                                                                  HBCA - stroma #> 64                                                                                                                              HBCA - epithelial #> 65                                                                                                                                  HBCA - immune #> 66                                                                                                                                  HBCA - global #> 67                                                                               Whole Taxonomy - MTG: Seattle Alzheimer's Disease Atlas (SEA-AD) #> 68                                                                                                 fetal and neonatal human small intestine cells #> 69                                                                                                                     Mouse Somatic Cell Lineage #> 70                                                                                                                      Human Immune Cell Lineage #> 71                                                                                                                                     Mouse Male #> 72                                                                                                                               Mouse Germ Cells #> 73                                                                                                                                     Human Male #> 74                                                                                                                     Human Somatic Cell Lineage #> 75                                                                                                                               Human Germ Cells #> 76                                                                                                                                   Human Female #> 77                                                                                                                                   Mouse Female #> 78                                                                                                                                 Adult duodenum #> 79                                                                                                                         Developing Human Atlas #> 80                                                                                                                         All non-neuronal cells #> 81                                                                                          A molecular single-cell lung atlas of lethal COVID-19 #> 82                                                                                                                                  TI epithelial #> 83                                                                                                                                      TI immune #> 84                                                                                                                                  colon stromal #> 85                                                                                                                               colon epithelial #>    dataset_total_cell_count #> 1                     31497 #> 2                     67070 #> 3                    286326 #> 4                    270855 #> 5                    167283 #> 6                     13497 #> 7                     27199 #> 8                     11505 #> 9                     24583 #> 10                     9522 #> 11                    16375 #> 12                    34004 #> 13                    12467 #> 14                    16037 #> 15                    10650 #> 16                    50115 #> 17                    13680 #> 18                     7124 #> 19                     5007 #> 20                    20263 #> 21                    15020 #> 22                    12297 #> 23                    11375 #> 24                     9641 #> 25                    30746 #> 26                    53275 #> 27                    35682 #> 28                    33664 #> 29                     9424 #> 30                     3596 #> 31                    45528 #> 32                    10734 #> 33                     3851 #> 34                     4880 #> 35                    21968 #> 36                    92676 #> 37                    83651 #> 38                    52638 #> 39                     8982 #> 40                   117346 #> 41                   208390 #> 42                   240804 #> 43                  1169213 #> 44                  2480956 #> 45                    72335 #> 46                    30789 #> 47                   714331 #> 48                    76567 #> 49                    12510 #> 50                   167598 #> 51                    40821 #> 52                    15481 #> 53                   105790 #> 54                     9471 #> 55                    15243 #> 56                     2303 #> 57                  2282447 #> 58                  1058909 #> 59                    82991 #> 60                     6044 #> 61                    20509 #> 62                     2576 #> 63                   413997 #> 64                   353167 #> 65                    23714 #> 66                   800198 #> 67                  1226855 #> 68                    36359 #> 69                    50933 #> 70                    20556 #> 71                    32889 #> 72                    10411 #> 73                   133811 #> 74                   191230 #> 75                    10993 #> 76                   213898 #> 77                    69709 #> 78                     5200 #> 79                   155232 #> 80                   888263 #> 81                   116313 #> 82                   154136 #> 83                   201072 #> 84                    39433 #> 85                    97788 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 508 rows ] census_data <- census$get(\"census_data\") all_experiments <- lapply(census_data$to_list(), function(x) census_data$get(x$name)) print(all_experiments) #> $mus_musculus #> <SOMAExperiment> #>   uri: s3://cellxgene-data-public/cell-census/2023-07-25/soma/census_data/mus_musculus  #>   arrays: obs*  #>   groups: ms*  #>  #> $homo_sapiens #> <SOMAExperiment> #>   uri: s3://cellxgene-data-public/cell-census/2023-07-25/soma/census_data/homo_sapiens  #>   arrays: obs*  #>   groups: ms* experiments_total_cells <- sum(sapply(all_experiments, function(x) {   nrow(x$obs$read(column_names = c(\"soma_joinid\"))$concat()) }))  print(paste(\"Found\", experiments_total_cells, \"cells in all experiments.\")) #> [1] \"Found 61656118 cells in all experiments.\" print(paste(   \"Found\", sum(as.vector(census_datasets$dataset_total_cell_count)),   \"cells in all datasets.\" )) #> [1] \"Found 61656118 cells in all datasets.\""},{"path":"/articles/census_datasets.html","id":"fetching-the-expression-data-from-a-single-dataset","dir":"Articles","previous_headings":"","what":"Fetching the expression data from a single dataset","title":"Census Datasets example","text":"Let’s pick one dataset slice census, turn Seurat -memory object. (requires Seurat package installed beforehand.) Create query mouse experiment, “RNA” measurement, dataset_id.","code":"census_datasets[census_datasets$dataset_id == \"0bd1a1de-3aee-40e0-b2ec-86c7a30c7149\", ] #>     soma_joinid                        collection_id    collection_name #> 523         522 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #>                collection_doi                           dataset_id #> 523 10.1038/s41586-020-2496-1 0bd1a1de-3aee-40e0-b2ec-86c7a30c7149 #>                                                                                        dataset_title #> 523 Bone marrow - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - 10x #>                             dataset_h5ad_path dataset_total_cell_count #> 523 0bd1a1de-3aee-40e0-b2ec-86c7a30c7149.h5ad                    40220 library(\"tiledbsoma\") obs_query <- SOMAAxisQuery$new(   value_filter = \"dataset_id == '0bd1a1de-3aee-40e0-b2ec-86c7a30c7149'\" ) expt_query <- census_data$get(\"mus_musculus\")$axis_query(   measurement_name = \"RNA\",   obs_query = obs_query ) dataset_seurat <- expt_query$to_seurat(c(counts = \"raw\")) print(dataset_seurat) #> An object of class Seurat  #> 52392 features across 40220 samples within 1 assay  #> Active assay: RNA (52392 features, 0 variable features) #>  2 layers present: counts, data"},{"path":"/articles/census_datasets.html","id":"downloading-the-original-source-h5ad-file-of-a-dataset","dir":"Articles","previous_headings":"","what":"Downloading the original source H5AD file of a dataset","title":"Census Datasets example","text":"can use cellxgene.census::get_source_h5ad_uri() API fetch URI pointing H5AD associated dataset_id. H5AD can download CZ CELLxGENE Discover, may contain additional data-submitter provided information included Census. can fetch location cloud directly download system. local H5AD file can used R using SeuratDisk’s anndata converter.","code":"# Option 1: Direct download download_source_h5ad(   dataset_id = \"0bd1a1de-3aee-40e0-b2ec-86c7a30c7149\",   file = \"/tmp/Tabula_Muris_Senis-bone_marrow.h5ad\",   overwrite = TRUE ) # Option 2: Get location and download via preferred method get_source_h5ad_uri(\"0bd1a1de-3aee-40e0-b2ec-86c7a30c7149\") #> $uri #> [1] \"s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/0bd1a1de-3aee-40e0-b2ec-86c7a30c7149.h5ad\" #>  #> $s3_region #> [1] \"us-west-2\""},{"path":"/articles/census_datasets.html","id":"close-the-census","dir":"Articles","previous_headings":"Downloading the original source H5AD file of a dataset","what":"Close the census","title":"Census Datasets example","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/articles/census_query_extract.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the census","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"cellxgene.census R package contains convenient API open version Census (default, newest stable version). can learn cellxgene.census methods accessing corresponding documentation, example ?cellxgene.census::open_soma.","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/census_query_extract.html","id":"querying-cell-metadata-obs","dir":"Articles","previous_headings":"","what":"Querying cell metadata (obs)","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"human gene metadata Census, RNA assays, located census$get(\"census_data\")$get(\"homo_sapiens\")$obs. SOMADataFrame can materialized R data frame (tibble) using .data.frame(obs$read()$concat()). mouse cell metadata census$get(\"census_data\")$get(\"mus_musculus\").obs. slicing cell metadata two relevant arguments can passed read(): column_names — character vector indicating metadata columns fetch. Expressions one comparisons Comparisons one <column> <op> <value> <column> <op> <column> Expressions can combine comparisons using && || op one < | > | <= | >= | == | != %% learn metadata columns available fetching filtering can directly look keys cell metadata. soma_joinid special SOMADataFrame column used join operations. definition columns can found Census schema. can used fetch specific columns specific rows matching condition. latter need know values looking priori. example let’s see possible values available sex. can load cell metadata fetching column sex. can see three different values sex, \"male\", \"female\" \"unknown\". information can fetch cell metatadata specific sex value, example \"unknown\". can use column_names value_filter perform specific queries. example let’s fetch disease column cell_type \"B cell\" tissue_general \"lung\".","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$obs$colnames() #>  [1] \"soma_joinid\"                              #>  [2] \"dataset_id\"                               #>  [3] \"assay\"                                    #>  [4] \"assay_ontology_term_id\"                   #>  [5] \"cell_type\"                                #>  [6] \"cell_type_ontology_term_id\"               #>  [7] \"development_stage\"                        #>  [8] \"development_stage_ontology_term_id\"       #>  [9] \"disease\"                                  #> [10] \"disease_ontology_term_id\"                 #> [11] \"donor_id\"                                 #> [12] \"is_primary_data\"                          #> [13] \"self_reported_ethnicity\"                  #> [14] \"self_reported_ethnicity_ontology_term_id\" #> [15] \"sex\"                                      #> [16] \"sex_ontology_term_id\"                     #> [17] \"suspension_type\"                          #> [18] \"tissue\"                                   #> [19] \"tissue_ontology_term_id\"                  #> [20] \"tissue_general\"                           #> [21] \"tissue_general_ontology_term_id\" unique(as.data.frame(census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(column_names = \"sex\")$concat())) #>            sex #> 1      unknown #> 670     female #> 385438    male as.data.frame(census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(value_filter = \"sex == 'unknown'\")$concat()) #>    soma_joinid                           dataset_id     assay assay_ontology_term_id #> 1            0 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 2            1 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 3            2 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 4            3 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 5            4 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 6            5 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 7            6 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 8            7 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 9            8 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 10           9 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 11          10 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #> 12          11 f171db61-e57e-4535-a06a-35d8b6ef8f2b 10x 3' v3            EFO:0009922 #>                        cell_type cell_type_ontology_term_id #> 1       syncytiotrophoblast cell                 CL:0000525 #> 2  placental villous trophoblast                 CL:2000060 #> 3       syncytiotrophoblast cell                 CL:0000525 #> 4       syncytiotrophoblast cell                 CL:0000525 #> 5       extravillous trophoblast                 CL:0008036 #> 6  placental villous trophoblast                 CL:2000060 #> 7       syncytiotrophoblast cell                 CL:0000525 #> 8       extravillous trophoblast                 CL:0008036 #> 9  placental villous trophoblast                 CL:2000060 #> 10      syncytiotrophoblast cell                 CL:0000525 #> 11      syncytiotrophoblast cell                 CL:0000525 #> 12      syncytiotrophoblast cell                 CL:0000525 #>                          development_stage development_stage_ontology_term_id disease #> 1  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 2  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 3  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 4  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 5  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 6  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 7  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 8  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 9  9th week post-fertilization human stage                     HsapDv:0000046  normal #> 10 9th week post-fertilization human stage                     HsapDv:0000046  normal #> 11 9th week post-fertilization human stage                     HsapDv:0000046  normal #> 12 9th week post-fertilization human stage                     HsapDv:0000046  normal #>    disease_ontology_term_id  donor_id is_primary_data self_reported_ethnicity #> 1              PATO:0000461 P13_fetus           FALSE                 unknown #> 2              PATO:0000461 P13_fetus           FALSE                 unknown #> 3              PATO:0000461 P13_fetus           FALSE                 unknown #> 4              PATO:0000461 P13_fetus           FALSE                 unknown #> 5              PATO:0000461 P13_fetus           FALSE                 unknown #> 6              PATO:0000461 P13_fetus           FALSE                 unknown #> 7              PATO:0000461 P13_fetus           FALSE                 unknown #> 8              PATO:0000461 P13_fetus           FALSE                 unknown #> 9              PATO:0000461 P13_fetus           FALSE                 unknown #> 10             PATO:0000461 P13_fetus           FALSE                 unknown #> 11             PATO:0000461 P13_fetus           FALSE                 unknown #> 12             PATO:0000461 P13_fetus           FALSE                 unknown #>    self_reported_ethnicity_ontology_term_id     sex sex_ontology_term_id #> 1                                   unknown unknown              unknown #> 2                                   unknown unknown              unknown #> 3                                   unknown unknown              unknown #> 4                                   unknown unknown              unknown #> 5                                   unknown unknown              unknown #> 6                                   unknown unknown              unknown #> 7                                   unknown unknown              unknown #> 8                                   unknown unknown              unknown #> 9                                   unknown unknown              unknown #> 10                                  unknown unknown              unknown #> 11                                  unknown unknown              unknown #> 12                                  unknown unknown              unknown #>    suspension_type          tissue tissue_ontology_term_id tissue_general #> 1          nucleus decidua basalis          UBERON:0000453       placenta #> 2          nucleus decidua basalis          UBERON:0000453       placenta #> 3          nucleus decidua basalis          UBERON:0000453       placenta #> 4          nucleus decidua basalis          UBERON:0000453       placenta #> 5          nucleus decidua basalis          UBERON:0000453       placenta #> 6          nucleus decidua basalis          UBERON:0000453       placenta #> 7          nucleus decidua basalis          UBERON:0000453       placenta #> 8          nucleus decidua basalis          UBERON:0000453       placenta #> 9          nucleus decidua basalis          UBERON:0000453       placenta #> 10         nucleus decidua basalis          UBERON:0000453       placenta #> 11         nucleus decidua basalis          UBERON:0000453       placenta #> 12         nucleus decidua basalis          UBERON:0000453       placenta #>    tissue_general_ontology_term_id #> 1                   UBERON:0001987 #> 2                   UBERON:0001987 #> 3                   UBERON:0001987 #> 4                   UBERON:0001987 #> 5                   UBERON:0001987 #> 6                   UBERON:0001987 #> 7                   UBERON:0001987 #> 8                   UBERON:0001987 #> 9                   UBERON:0001987 #> 10                  UBERON:0001987 #> 11                  UBERON:0001987 #> 12                  UBERON:0001987 #>  [ reached 'max' / getOption(\"max.print\") -- omitted 3251322 rows ] cell_metadata_b_cell <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   value_filter = \"cell_type == 'B cell' & tissue_general == 'lung'\",   column_names = \"disease\" )  cell_metadata_b_cell <- as.data.frame(cell_metadata_b_cell$concat())  table(cell_metadata_b_cell) #> disease #> chronic obstructive pulmonary disease                              COVID-19  #>                                  6369                                  2729  #>          hypersensitivity pneumonitis             interstitial lung disease  #>                                    52                                   376  #>                   lung adenocarcinoma             lung large cell carcinoma  #>                                 62351                                  1534  #>              lymphangioleiomyomatosis         non-small cell lung carcinoma  #>                                   133                                 17484  #>   non-specific interstitial pneumonia                                normal  #>                                   231                                 25461  #>                 pleomorphic carcinoma                             pneumonia  #>                                  1210                                    50  #>                   pulmonary emphysema                    pulmonary fibrosis  #>                                  1512                                  6798  #>                 pulmonary sarcoidosis             small cell lung carcinoma  #>                                     6                                   583  #>          squamous cell lung carcinoma  #>                                 11920"},{"path":"/articles/census_query_extract.html","id":"querying-gene-metadata-var","dir":"Articles","previous_headings":"","what":"Querying gene metadata (var)","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"human gene metadata Census located census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var. Similarly cell metadata, SOMADataFrame thus can also use method read(). mouse gene metadata census$get(\"census_data\")$get(\"mus_musculus\")$ms$get(\"RNA\")$var. Let’s take look metadata available column selection row filtering. exception soma_joinid columns defined Census schema. Similarly cell metadata, can use operations learn fetch gene metadata. example, get feature_name feature_length genes \"ENSG00000161798\" \"ENSG00000188229\" can following.","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var$colnames() #> [1] \"soma_joinid\"    \"feature_id\"     \"feature_name\"   \"feature_length\" var_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var$read(   value_filter = \"feature_id %in% c('ENSG00000161798', 'ENSG00000188229')\",   column_names = c(\"feature_name\", \"feature_length\") )  as.data.frame(var_df$concat()) #>   feature_name feature_length #> 1         AQP5           1884 #> 2       TUBB4B           2037"},{"path":"/articles/census_query_extract.html","id":"querying-expression-data-as-seurat","dir":"Articles","previous_headings":"","what":"Querying expression data as Seurat","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"convenient way query fetch expression data use get_seurat method cellxgene.census API. method combines column selection value filtering described obtain slices expression data based metadata queries. method return Seurat object, takes input census object, string organism, cell gene metadata can specify filters column selection described following arguments: obs_column_names — character vector indicating columns select cell metadata. obs_value_filter — expression selection conditions fetch cells meeting criteria. var_column_names — character vector indicating columns select gene metadata. var_value_filter — expression selection conditions fetch genes meeting criteria. example want fetch expression data : Genes \"ENSG00000161798\" \"ENSG00000188229\". \"B cells\" \"lung\" \"COVID-19\". gene metadata adding sex cell metadata. full description refer ?cellxgene.census::get_seurat.","code":"library(\"Seurat\")  seurat_obj <- get_seurat(   census, \"Homo sapiens\",   obs_column_names = c(\"cell_type\", \"tissue_general\", \"disease\", \"sex\"),   var_value_filter = \"feature_id %in% c('ENSG00000161798', 'ENSG00000188229')\",   obs_value_filter = \"cell_type == 'B cell' & tissue_general == 'lung' & disease == 'COVID-19'\" ) seurat_obj #> An object of class Seurat  #> 2 features across 2729 samples within 1 assay  #> Active assay: RNA (2 features, 0 variable features) #>  2 layers present: counts, data head(seurat_obj[[]]) #>                orig.ident nCount_RNA nFeature_RNA cell_type tissue_general  disease #> cell5946018 SeuratProject          0            0    B cell           lung COVID-19 #> cell5948526 SeuratProject          1            1    B cell           lung COVID-19 #> cell5949180 SeuratProject          0            0    B cell           lung COVID-19 #> cell5949686 SeuratProject          0            0    B cell           lung COVID-19 #> cell5950730 SeuratProject          0            0    B cell           lung COVID-19 #> cell5952197 SeuratProject          0            0    B cell           lung COVID-19 #>                 sex #> cell5946018    male #> cell5948526 unknown #> cell5949180    male #> cell5949686 unknown #> cell5950730    male #> cell5952197 unknown head(seurat_obj$RNA[[]]) #>                 feature_name feature_length #> ENSG00000161798         AQP5           1884 #> ENSG00000188229       TUBB4B           2037"},{"path":"/articles/census_query_extract.html","id":"querying-expression-data-as-singlecellexperiment","dir":"Articles","previous_headings":"","what":"Querying expression data as SingleCellExperiment","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"Similarly previous section, get_single_cell_experiment method cellxgene.census API. behaves exactly get_seurat returns SingleCellExperiment object. example, repeat query can simply following. full description refer ?cellxgene.census::get_single_cell_experiment.","code":"library(\"SingleCellExperiment\")  sce_obj <- get_single_cell_experiment(   census, \"Homo sapiens\",   obs_column_names = c(\"cell_type\", \"tissue_general\", \"disease\", \"sex\"),   var_value_filter = \"feature_id %in% c('ENSG00000161798', 'ENSG00000188229')\",   obs_value_filter = \"cell_type == 'B cell' & tissue_general == 'lung' & disease == 'COVID-19'\" ) sce_obj #> class: SingleCellExperiment  #> dim: 2 2729  #> metadata(0): #> assays(1): counts #> rownames(2): ENSG00000161798 ENSG00000188229 #> rowData names(2): feature_name feature_length #> colnames(2729): obs5946018 obs5948526 ... obs48038140 obs48038164 #> colData names(4): cell_type tissue_general disease sex #> reducedDimNames(0): #> mainExpName: RNA #> altExpNames(0): head(colData(sce_obj)) #> DataFrame with 6 rows and 4 columns #>              cell_type tissue_general     disease         sex #>            <character>    <character> <character> <character> #> obs5946018      B cell           lung    COVID-19        male #> obs5948526      B cell           lung    COVID-19     unknown #> obs5949180      B cell           lung    COVID-19        male #> obs5949686      B cell           lung    COVID-19     unknown #> obs5950730      B cell           lung    COVID-19        male #> obs5952197      B cell           lung    COVID-19     unknown head(rowData(sce_obj)) #> DataFrame with 2 rows and 2 columns #>                 feature_name feature_length #>                  <character>      <integer> #> ENSG00000161798         AQP5           1884 #> ENSG00000188229       TUBB4B           2037"},{"path":"/articles/census_query_extract.html","id":"close-the-census","dir":"Articles","previous_headings":"Querying expression data as SingleCellExperiment","what":"Close the census","title":"Querying and fetching the single-cell data and cell/gene metadata","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/articles/comp_bio_census_info.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the Census","title":"Learning about the CZ CELLxGENE Census","text":"cellxgene.census R package contains convenient open_soma() API open version Census (stable default). can learn cellxgene.census methods accessing corresponding documentation, example ?cellxgene.census::open_soma.","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/comp_bio_census_info.html","id":"census-organization","dir":"Articles","previous_headings":"","what":"Census organization","title":"Learning about the CZ CELLxGENE Census","text":"Census schema defines structure Census. short, can think Census structured collection items stores different pieces information. items parent collection SOMA objects various types can accessed TileDB-SOMA API (documentation). cellxgene.census package contains convenient wrappers TileDB-SOMA API. example function used open Census: cellxgene_census.open_soma().","code":""},{"path":"/articles/comp_bio_census_info.html","id":"main-census-components","dir":"Articles","previous_headings":"Census organization","what":"Main Census components","title":"Learning about the CZ CELLxGENE Census","text":"command created census, SOMACollection, R6 class providing key-value associative map. get() method can access two top-level collection members, census_info census_data, instances SOMACollection.","code":""},{"path":"/articles/comp_bio_census_info.html","id":"census-summary-info","dir":"Articles","previous_headings":"Census organization","what":"Census summary info","title":"Learning about the CZ CELLxGENE Census","text":"census$get(\"census_info\")$get(\"summary\"): data frame high-level information Census, e.g. build date, total cell count, etc. census$get(\"census_info\")$get(\"datasets\"): data frame datasets CELLxGENE Discover used create Census. census$get(\"census_info\")$get(\"summary_cell_counts\"): data frame cell counts stratified relevant cell metadata Census data Data organism stored independent SOMAExperiment objects specialized form SOMACollection. store data matrix (cell genes), cell metadata, gene metadata, useful components covered notebook. data organized one organism – Homo sapiens: census$get(\"census_data\")$get(\"homo_sapiens\")$obs: Cell metadata census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\"): Data matrices, currently … census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$X$get(\"raw\"): matrix raw counts SOMASparseNDArray census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var: Gene Metadata","code":""},{"path":"/articles/comp_bio_census_info.html","id":"cell-metadata","dir":"Articles","previous_headings":"","what":"Cell metadata","title":"Learning about the CZ CELLxGENE Census","text":"can obtain cell metadata variables directly querying columns corresponding SOMADataFrame. variables can used querying Census case want work specific cells. variables defined CELLxGENE dataset schema except following: soma_joinid: SOMA-defined value use join operations. dataset_id: dataset id encoded census$get(\"census_info\")$get(\"datasets\"). tissue_general tissue_general_ontology_term_id: high-level tissue mapping.","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$obs$colnames() #>  [1] \"soma_joinid\"                              #>  [2] \"dataset_id\"                               #>  [3] \"assay\"                                    #>  [4] \"assay_ontology_term_id\"                   #>  [5] \"cell_type\"                                #>  [6] \"cell_type_ontology_term_id\"               #>  [7] \"development_stage\"                        #>  [8] \"development_stage_ontology_term_id\"       #>  [9] \"disease\"                                  #> [10] \"disease_ontology_term_id\"                 #> [11] \"donor_id\"                                 #> [12] \"is_primary_data\"                          #> [13] \"self_reported_ethnicity\"                  #> [14] \"self_reported_ethnicity_ontology_term_id\" #> [15] \"sex\"                                      #> [16] \"sex_ontology_term_id\"                     #> [17] \"suspension_type\"                          #> [18] \"tissue\"                                   #> [19] \"tissue_ontology_term_id\"                  #> [20] \"tissue_general\"                           #> [21] \"tissue_general_ontology_term_id\""},{"path":"/articles/comp_bio_census_info.html","id":"gene-metadata","dir":"Articles","previous_headings":"","what":"Gene metadata","title":"Learning about the CZ CELLxGENE Census","text":"Similarly, can obtain gene metadata variables directly querying columns corresponding SOMADataFrame. variables can use querying Census case specific genes interested . variables defined CELLxGENE dataset schema except following: soma_joinid: SOMA-defined value use join operations. feature_length: length base pairs gene.","code":"census$get(\"census_data\")$get(\"homo_sapiens\")$ms$get(\"RNA\")$var$colnames() #> [1] \"soma_joinid\"    \"feature_id\"     \"feature_name\"   \"feature_length\""},{"path":"/articles/comp_bio_census_info.html","id":"census-summary-content-tables","dir":"Articles","previous_headings":"","what":"Census summary content tables","title":"Learning about the CZ CELLxGENE Census","text":"can take quick look high-level Census information looking census$get(\"census_info\")$get(\"summary\"): special interest label-value combinations : total_cell_count total number cells Census. unique_cell_count number unique cells, cells may present twice due meta-analysis consortia-like data. number_donors_homo_sapiens number_donors_mus_musculus number individuals human mouse. guaranteed unique one individual ID may present identical different datasets.","code":"as.data.frame(census$get(\"census_info\")$get(\"summary\")$read()$concat()) #>   soma_joinid                      label      value #> 1           0      census_schema_version      1.0.0 #> 2           1          census_build_date 2023-07-25 #> 3           2     dataset_schema_version      3.0.0 #> 4           3           total_cell_count   61656118 #> 5           4          unique_cell_count   37447773 #> 6           5 number_donors_homo_sapiens      13035 #> 7           6 number_donors_mus_musculus       1417"},{"path":"/articles/comp_bio_census_info.html","id":"cell-counts-by-cell-metadata","dir":"Articles","previous_headings":"Census summary content tables","what":"Cell counts by cell metadata","title":"Learning about the CZ CELLxGENE Census","text":"looking census$get(\"census_info)$get(\"summary_cell_counts\") can get general idea cell counts stratified relevant cell metadata. cell metadata included table, can take look cell gene metadata available sections “Cell metadata” “Gene metadata”. line retrieves table casts R data frame: combination organism values category cell metadata can take look total_cell_count unique_cell_count cell counts combination. values category specified ontology_term_id label, value’s IDs labels, respectively.","code":"census_counts <- as.data.frame(census$get(\"census_info\")$get(\"summary_cell_counts\")$read()$concat()) head(census_counts) #>   soma_joinid     organism category ontology_term_id unique_cell_count total_cell_count #> 1           0 Homo sapiens      all               na          33364242         56400873 #> 2           1 Homo sapiens    assay      EFO:0008722            264166           279635 #> 3           2 Homo sapiens    assay      EFO:0008780             25652            51304 #> 4           3 Homo sapiens    assay      EFO:0008919             89477           206754 #> 5           4 Homo sapiens    assay      EFO:0008931             78750           188248 #> 6           5 Homo sapiens    assay      EFO:0008953              4693             9386 #>        label #> 1         na #> 2   Drop-seq #> 3     inDrop #> 4   Seq-Well #> 5 Smart-seq2 #> 6   STRT-seq"},{"path":"/articles/comp_bio_census_info.html","id":"example-cell-metadata-included-in-the-summary-counts-table","dir":"Articles","previous_headings":"Census summary content tables > Cell counts by cell metadata","what":"Example: cell metadata included in the summary counts table","title":"Learning about the CZ CELLxGENE Census","text":"get available cell metadata summary counts table can following. Remember cell metadata available, variables omitted creation table.","code":"t(table(census_counts$organism, census_counts$category)) #>                           #>                           Homo sapiens Mus musculus #>   all                                1            1 #>   assay                             19            9 #>   cell_type                        613          248 #>   disease                           64            5 #>   self_reported_ethnicity           26            1 #>   sex                                3            3 #>   suspension_type                    1            1 #>   tissue                           220           66 #>   tissue_general                    54           27"},{"path":"/articles/comp_bio_census_info.html","id":"example-cell-counts-for-each-sequencing-assay-in-human-data","dir":"Articles","previous_headings":"Census summary content tables > Cell counts by cell metadata","what":"Example: cell counts for each sequencing assay in human data","title":"Learning about the CZ CELLxGENE Census","text":"get cell counts sequencing assay type human data, can perform following operations:","code":"human_assay_counts <- census_counts[census_counts$organism == \"Homo sapiens\" & census_counts$category == \"assay\", ] human_assay_counts <- human_assay_counts[order(human_assay_counts$total_cell_count, decreasing = TRUE), ]"},{"path":"/articles/comp_bio_census_info.html","id":"example-number-of-microglial-cells-in-the-census","dir":"Articles","previous_headings":"Census summary content tables > Cell counts by cell metadata","what":"Example: number of microglial cells in the Census","title":"Learning about the CZ CELLxGENE Census","text":"specific term categories shown can directly find number cells term.","code":"census_counts[census_counts$label == \"microglial cell\", ] #>      soma_joinid     organism  category ontology_term_id unique_cell_count #> 70            69 Homo sapiens cell_type       CL:0000129            268114 #> 1039        1038 Mus musculus cell_type       CL:0000129             48998 #>      total_cell_count           label #> 70             370771 microglial cell #> 1039            62617 microglial cell"},{"path":"/articles/comp_bio_census_info.html","id":"understanding-census-contents-beyond-the-summary-tables","dir":"Articles","previous_headings":"","what":"Understanding Census contents beyond the summary tables","title":"Learning about the CZ CELLxGENE Census","text":"using pre-computed tables census$get(\"census_info\") easy quick way understand contents Census, falls short want learn certain slices Census. example, may want learn : cell types available human liver? total number cells lung datasets stratified sequencing technology? sex distribution cells brain mouse? diseases available T cells? questions can answered directly querying cell metadata shown examples .","code":""},{"path":"/articles/comp_bio_census_info.html","id":"example-all-cell-types-available-in-human","dir":"Articles","previous_headings":"Understanding Census contents beyond the summary tables","what":"Example: all cell types available in human","title":"Learning about the CZ CELLxGENE Census","text":"exemplify process accessing slicing cell metadata summary stats, let’s start trivial example take look human cell types available Census: number rows total number cells humans. Now, wish get cell counts per cell type can work data frame. addition, focus cells marked is_primary_data=TRUE ensures de-duplicate cells appear CELLxGENE Discover. number unique cells. Now let’s look counts per cell type: shows abundant cell types “glutamatergic neuron”, “CD8-positive, alpha-beta T cell”, “CD4-positive, alpha-beta T cell”. Now let’s take look number unique cell types: total number different cell types human. information example can quickly obtained summary table census$get(\"census-info\")$get(\"summary_cell_counts\"). examples complex can achieved accessing cell metadata.","code":"obs_df <-   census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(column_names = c(\"cell_type\", \"is_primary_data\")) as.data.frame(obs_df$concat()) #>                         cell_type is_primary_data #> 1        syncytiotrophoblast cell           FALSE #> 2   placental villous trophoblast           FALSE #> 3        syncytiotrophoblast cell           FALSE #> 4        syncytiotrophoblast cell           FALSE #> 5        extravillous trophoblast           FALSE #> 6   placental villous trophoblast           FALSE #> 7        syncytiotrophoblast cell           FALSE #> 8        extravillous trophoblast           FALSE #> 9   placental villous trophoblast           FALSE #> 10       syncytiotrophoblast cell           FALSE #> 11       syncytiotrophoblast cell           FALSE #> 12       syncytiotrophoblast cell           FALSE #> 13  placental villous trophoblast           FALSE #> 14  placental villous trophoblast           FALSE #> 15       syncytiotrophoblast cell           FALSE #> 16       syncytiotrophoblast cell           FALSE #> 17       syncytiotrophoblast cell           FALSE #> 18       syncytiotrophoblast cell           FALSE #> 19       syncytiotrophoblast cell           FALSE #> 20       extravillous trophoblast           FALSE #> 21       syncytiotrophoblast cell           FALSE #> 22       syncytiotrophoblast cell           FALSE #> 23  placental villous trophoblast           FALSE #> 24       syncytiotrophoblast cell           FALSE #> 25       syncytiotrophoblast cell           FALSE #> 26       syncytiotrophoblast cell           FALSE #> 27       syncytiotrophoblast cell           FALSE #> 28       syncytiotrophoblast cell           FALSE #> 29       syncytiotrophoblast cell           FALSE #> 30       syncytiotrophoblast cell           FALSE #> 31  placental villous trophoblast           FALSE #> 32         trophoblast giant cell           FALSE #> 33       syncytiotrophoblast cell           FALSE #> 34  placental villous trophoblast           FALSE #> 35       syncytiotrophoblast cell           FALSE #> 36       extravillous trophoblast           FALSE #> 37  placental villous trophoblast           FALSE #> 38  placental villous trophoblast           FALSE #> 39       syncytiotrophoblast cell           FALSE #> 40       syncytiotrophoblast cell           FALSE #> 41       syncytiotrophoblast cell           FALSE #> 42       syncytiotrophoblast cell           FALSE #> 43       syncytiotrophoblast cell           FALSE #> 44  placental villous trophoblast           FALSE #> 45  placental villous trophoblast           FALSE #> 46       syncytiotrophoblast cell           FALSE #> 47  placental villous trophoblast           FALSE #> 48       syncytiotrophoblast cell           FALSE #> 49       syncytiotrophoblast cell           FALSE #> 50       syncytiotrophoblast cell           FALSE #> 51       syncytiotrophoblast cell           FALSE #> 52       syncytiotrophoblast cell           FALSE #> 53       syncytiotrophoblast cell           FALSE #> 54       syncytiotrophoblast cell           FALSE #> 55  placental villous trophoblast           FALSE #> 56  placental villous trophoblast           FALSE #> 57  placental villous trophoblast           FALSE #> 58       syncytiotrophoblast cell           FALSE #> 59       syncytiotrophoblast cell           FALSE #> 60       extravillous trophoblast           FALSE #> 61  placental villous trophoblast           FALSE #> 62       extravillous trophoblast           FALSE #> 63       syncytiotrophoblast cell           FALSE #> 64       syncytiotrophoblast cell           FALSE #> 65       syncytiotrophoblast cell           FALSE #> 66  placental villous trophoblast           FALSE #> 67       syncytiotrophoblast cell           FALSE #> 68  placental villous trophoblast           FALSE #> 69       syncytiotrophoblast cell           FALSE #> 70       syncytiotrophoblast cell           FALSE #> 71       syncytiotrophoblast cell           FALSE #> 72       syncytiotrophoblast cell           FALSE #> 73  placental villous trophoblast           FALSE #> 74  placental villous trophoblast           FALSE #> 75  placental villous trophoblast           FALSE #> 76  placental villous trophoblast           FALSE #> 77       syncytiotrophoblast cell           FALSE #> 78       syncytiotrophoblast cell           FALSE #> 79       syncytiotrophoblast cell           FALSE #> 80  placental villous trophoblast           FALSE #> 81       syncytiotrophoblast cell           FALSE #> 82       syncytiotrophoblast cell           FALSE #> 83       syncytiotrophoblast cell           FALSE #> 84  placental villous trophoblast           FALSE #> 85       extravillous trophoblast           FALSE #> 86       syncytiotrophoblast cell           FALSE #> 87       syncytiotrophoblast cell           FALSE #> 88  placental villous trophoblast           FALSE #> 89  placental villous trophoblast           FALSE #> 90       syncytiotrophoblast cell           FALSE #> 91       extravillous trophoblast           FALSE #> 92  placental villous trophoblast           FALSE #> 93       syncytiotrophoblast cell           FALSE #> 94       syncytiotrophoblast cell           FALSE #> 95       syncytiotrophoblast cell           FALSE #> 96       extravillous trophoblast           FALSE #> 97       syncytiotrophoblast cell           FALSE #> 98  placental villous trophoblast           FALSE #> 99  placental villous trophoblast           FALSE #> 100      extravillous trophoblast           FALSE #> 101 placental villous trophoblast           FALSE #> 102      syncytiotrophoblast cell           FALSE #> 103      syncytiotrophoblast cell           FALSE #> 104      extravillous trophoblast           FALSE #> 105 placental villous trophoblast           FALSE #> 106 placental villous trophoblast           FALSE #> 107 placental villous trophoblast           FALSE #> 108 placental villous trophoblast           FALSE #> 109      syncytiotrophoblast cell           FALSE #> 110 placental villous trophoblast           FALSE #> 111 placental villous trophoblast           FALSE #> 112      extravillous trophoblast           FALSE #> 113      extravillous trophoblast           FALSE #> 114      syncytiotrophoblast cell           FALSE #> 115 placental villous trophoblast           FALSE #> 116      extravillous trophoblast           FALSE #> 117 placental villous trophoblast           FALSE #> 118 placental villous trophoblast           FALSE #> 119      syncytiotrophoblast cell           FALSE #> 120      syncytiotrophoblast cell           FALSE #> 121      syncytiotrophoblast cell           FALSE #> 122      syncytiotrophoblast cell           FALSE #> 123      syncytiotrophoblast cell           FALSE #> 124 placental villous trophoblast           FALSE #> 125 placental villous trophoblast           FALSE #> 126 placental villous trophoblast           FALSE #> 127      extravillous trophoblast           FALSE #> 128      extravillous trophoblast           FALSE #>  [ reached 'max' / getOption(\"max.print\") -- omitted 56400745 rows ] obs_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   column_names = \"cell_type\",   value_filter = \"is_primary_data == TRUE\" )  obs_df <- as.data.frame(obs_df$concat()) nrow(obs_df) #> [1] 33364242 human_cell_type_counts <- table(obs_df$cell_type) sort(human_cell_type_counts, decreasing = TRUE)[1:10] #>  #>                          neuron            glutamatergic neuron  #>                         2673669                         1541605  #> CD4-positive, alpha-beta T cell CD8-positive, alpha-beta T cell  #>                         1258976                         1235987  #>              classical monocyte                          B cell  #>                         1030996                          908651  #>                     native cell             natural killer cell  #>                          889262                          768755  #>                      macrophage                 oligodendrocyte  #>                          721687                          710242 length(human_cell_type_counts) #> [1] 599"},{"path":"/articles/comp_bio_census_info.html","id":"example-cell-types-available-in-human-liver","dir":"Articles","previous_headings":"Understanding Census contents beyond the summary tables","what":"Example: cell types available in human liver","title":"Learning about the CZ CELLxGENE Census","text":"Similar example , can learn cell types available specific tissue, e.g. liver. achieve goal just need limit cell metadata tissue. use information cell metadata variable tissue_general. variable contains high-level tissue label cells Census: cell types cell counts human liver.","code":"obs_liver_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   column_names = \"cell_type\",   value_filter = \"is_primary_data == TRUE & tissue_general == 'liver'\" )  obs_liver_df <- as.data.frame(obs_liver_df$concat())  sort(table(obs_liver_df$cell_type), decreasing = TRUE)[1:10] #>  #>                          T cell                     hepatoblast  #>                           85739                           58447  #>                 neoplastic cell                    erythroblast  #>                           52431                           45605  #>                        monocyte                      hepatocyte  #>                           31388                           28309  #>             natural killer cell    periportal region hepatocyte  #>                           26871                           23509  #>                      macrophage centrilobular region hepatocyte  #>                           16707                           15819"},{"path":"/articles/comp_bio_census_info.html","id":"example-diseased-t-cells-in-human-tissues","dir":"Articles","previous_headings":"Understanding Census contents beyond the summary tables","what":"Example: diseased T cells in human tissues","title":"Learning about the CZ CELLxGENE Census","text":"example going get counts diseased cells annotated T cells. sake example focus “CD8-positive, alpha-beta T cell” “CD4-positive, alpha-beta T cell”: cell counts annotated indicated disease across human tissues “CD8-positive, alpha-beta T cell” “CD4-positive, alpha-beta T cell”.","code":"obs_t_cells_df <- census$get(\"census_data\")$get(\"homo_sapiens\")$obs$read(   column_names = c(\"disease\", \"tissue_general\"),   value_filter = \"is_primary_data == TRUE & disease != 'normal' & cell_type %in% c('CD8-positive, alpha-beta T cell', 'CD4-positive, alpha-beta T cell')\" )  obs_t_cells_df <- as.data.frame(obs_t_cells_df$concat())  print(table(obs_t_cells_df)) #>                                        tissue_general #> disease                                 adipose tissue adrenal gland  blood blood clot #>   B-cell non-Hodgkin lymphoma                        0             0  62499          0 #>   breast cancer                                      0             0      0          0 #>   chronic obstructive pulmonary disease              0             0      0          0 #>   chronic rhinitis                                   0             0      0          0 #>   clear cell renal carcinoma                         0             0   6548          0 #>   COVID-19                                           0             0 819428          0 #>   Crohn disease                                      0             0      0          0 #>   cystic fibrosis                                    0             0      0          0 #>   Down syndrome                                      0             0      0          0 #>   follicular lymphoma                                0             0      0          0 #>   influenza                                          0             0   8871          0 #>   interstitial lung disease                          0             0      0          0 #>   kidney benign neoplasm                             0             0     20          0 #>   kidney oncocytoma                                  0             0     16          0 #>   lung adenocarcinoma                                0           205      0          0 #>                                        tissue_general #> disease                                 bone marrow  brain breast  colon kidney  liver #>   B-cell non-Hodgkin lymphoma                     0      0      0      0      0      0 #>   breast cancer                                   0      0   1850      0      0      0 #>   chronic obstructive pulmonary disease           0      0      0      0      0      0 #>   chronic rhinitis                                0      0      0      0      0      0 #>   clear cell renal carcinoma                      0      0      0      0  20540      0 #>   COVID-19                                        0      0      0      0      0      0 #>   Crohn disease                                   0      0      0  17490      0      0 #>   cystic fibrosis                                 0      0      0      0      0      0 #>   Down syndrome                                 181      0      0      0      0      0 #>   follicular lymphoma                             0      0      0      0      0      0 #>   influenza                                       0      0      0      0      0      0 #>   interstitial lung disease                       0      0      0      0      0      0 #>   kidney benign neoplasm                          0      0      0      0     10      0 #>   kidney oncocytoma                               0      0      0      0   2408      0 #>   lung adenocarcinoma                             0   3274      0      0      0    507 #>                                        tissue_general #> disease                                   lung lymph node   nose pleural fluid #>   B-cell non-Hodgkin lymphoma                0          0      0             0 #>   breast cancer                              0          0      0             0 #>   chronic obstructive pulmonary disease   9382          0      0             0 #>   chronic rhinitis                           0          0    909             0 #>   clear cell renal carcinoma                 0         36      0             0 #>   COVID-19                               30578          0     13             0 #>   Crohn disease                              0          0      0             0 #>   cystic fibrosis                            7          0      0             0 #>   Down syndrome                              0          0      0             0 #>   follicular lymphoma                        0       1089      0             0 #>   influenza                                  0          0      0             0 #>   interstitial lung disease               1803          0      0             0 #>   kidney benign neoplasm                     0          0      0             0 #>   kidney oncocytoma                          0          0      0             0 #>   lung adenocarcinoma                   215013      24969      0         11558 #>                                        tissue_general #> disease                                 respiratory system saliva small intestine #>   B-cell non-Hodgkin lymphoma                            0      0               0 #>   breast cancer                                          0      0               0 #>   chronic obstructive pulmonary disease                  0      0               0 #>   chronic rhinitis                                       0      0               0 #>   clear cell renal carcinoma                             0      0               0 #>   COVID-19                                               4     41               0 #>   Crohn disease                                          0      0           52029 #>   cystic fibrosis                                        0      0               0 #>   Down syndrome                                          0      0               0 #>   follicular lymphoma                                    0      0               0 #>   influenza                                              0      0               0 #>   interstitial lung disease                              0      0               0 #>   kidney benign neoplasm                                 0      0               0 #>   kidney oncocytoma                                      0      0               0 #>   lung adenocarcinoma                                    0      0               0 #>  [ reached getOption(\"max.print\") -- omitted 10 rows ]"},{"path":"/articles/comp_bio_data_integration.html","id":"finding-and-fetching-data-from-mouse-liver-10x-genomics-and-smart-seq2","dir":"Articles","previous_headings":"","what":"Finding and fetching data from mouse liver (10X Genomics and Smart-Seq2)","title":"Integrating multi-dataset slices of data with Seurat","text":"Let’s load packages needed notebook. Now can open Census. notebook use Tabula Muris Senis data liver contains cells 10X Genomics Smart-Seq2 technologies. Let’s query datasets table Census filtering collection_name “Tabula Muris Senis” dataset_title “liver”. Now can use values dataset_id query load Seurat object cells datasets. can check cell counts 10X Genomics Smart-Seq2 data looking assay metadata.","code":"library(\"cellxgene.census\") library(\"Seurat\") census <- open_soma() census_datasets <- census$get(\"census_info\")$get(\"datasets\") census_datasets <- census_datasets$read(value_filter = \"collection_name == 'Tabula Muris Senis'\") census_datasets <- as.data.frame(census_datasets$concat())  # Print rows with liver data census_datasets[grep(\"Liver\", census_datasets$dataset_title), ] #>    soma_joinid                        collection_id    collection_name #> 14         525 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #> 35         547 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #>               collection_doi                           dataset_id #> 14 10.1038/s41586-020-2496-1 4546e757-34d0-4d17-be06-538318925fcd #> 35 10.1038/s41586-020-2496-1 6202a243-b713-4e12-9ced-c387f8483dea #>                                                                                        dataset_title #> 14 Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2 #> 35        Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - 10x #>                            dataset_h5ad_path dataset_total_cell_count #> 14 4546e757-34d0-4d17-be06-538318925fcd.h5ad                     2859 #> 35 6202a243-b713-4e12-9ced-c387f8483dea.h5ad                     7294 tabula_muris_liver_ids <- c(\"4546e757-34d0-4d17-be06-538318925fcd\", \"6202a243-b713-4e12-9ced-c387f8483dea\")  seurat_obj <- get_seurat(   census,   organism = \"Mus musculus\",   obs_value_filter = \"dataset_id %in% tabula_muris_liver_ids\" ) table(seurat_obj$assay) #>  #>  10x 3' v2 Smart-seq2  #>       7294       2859"},{"path":"/articles/comp_bio_data_integration.html","id":"gene-length-normalization-of-smart-seq2-data-","dir":"Articles","previous_headings":"","what":"Gene-length normalization of Smart-Seq2 data.","title":"Integrating multi-dataset slices of data with Seurat","text":"Smart-seq2 read counts normalized gene length. Lets first get gene lengths var.feature_length. Now can use normalize Smart-seq data. let’s split object assay. normalize Smart-seq slice using gene lengths merge back single object.","code":"smart_seq_gene_lengths <- seurat_obj$RNA[[]]$feature_length seurat_obj.list <- SplitObject(seurat_obj, split.by = \"assay\") seurat_obj.list[[\"Smart-seq2\"]][[\"RNA\"]]@counts <- seurat_obj.list[[\"Smart-seq2\"]][[\"RNA\"]]@counts / smart_seq_gene_lengths seurat_obj <- merge(seurat_obj.list[[1]], seurat_obj.list[[2]])"},{"path":"/articles/comp_bio_data_integration.html","id":"integration-with-seurat","dir":"Articles","previous_headings":"","what":"Integration with Seurat","title":"Integrating multi-dataset slices of data with Seurat","text":"use native integration capabilities Seurat. comprehensive usage best practices Seurat intergation please refer doc site Seurat.","code":""},{"path":"/articles/comp_bio_data_integration.html","id":"inspecting-data-prior-to-integration","dir":"Articles","previous_headings":"Integration with Seurat","what":"Inspecting data prior to integration","title":"Integrating multi-dataset slices of data with Seurat","text":"Let’s take look strength batch effects data. perform embedding visualization via UMAP. Let’s basic data normalization variable gene selection now perform PCA UMAP   can see batch effects strong cells cluster primarily assay cell_type. Properly integrated embedding principle cluster primarily cell_type, assay best randomly distributed.","code":"seurat_obj <- SCTransform(seurat_obj) seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = \"vst\", nfeatures = 2000) seurat_obj <- RunPCA(seurat_obj, features = VariableFeatures(object = seurat_obj)) seurat_obj <- RunUMAP(seurat_obj, dims = 1:30) # By assay p1 <- DimPlot(seurat_obj, reduction = \"umap\", group.by = \"assay\") p1 # By cell type p2 <- DimPlot(seurat_obj, reduction = \"umap\", group.by = \"cell_type\") p2"},{"path":"/articles/comp_bio_data_integration.html","id":"data-integration-with-seurat","dir":"Articles","previous_headings":"Integration with Seurat","what":"Data integration with Seurat","title":"Integrating multi-dataset slices of data with Seurat","text":"Whenever query fetch Census data multiple datasets integration needs performed evidenced batch effects observed. paramaters Seurat used notebook selected model run quickly. best practices integration single-cell data using Seurat please refer documentation page. seurat_d reading article integrated cell atlas human lung health disease Sikkema et al. perfomed integration 43 datasets Lung. focus metadata Census can batch information integration.","code":""},{"path":"/articles/comp_bio_data_integration.html","id":"integration-across-datasets-using-dataset_id","dir":"Articles","previous_headings":"Integration with Seurat > Data integration with Seurat","what":"Integration across datasets using dataset_id","title":"Integrating multi-dataset slices of data with Seurat","text":"cells Census annotated dataset come \"dataset_id\". great place start integration. let’s run Seurat integration pipeline. First define model batch set dataset_id. Firs normalize select variable genes seperated batch key dataset_id Now perform integration. Let’s inspect results normalization UMAP visulization. plot UMAP.   Great! can see clustering longer mainly driven assay, albeit still contributing . Great! can see clustering longer mainly driven assay, albeit still contributing .","code":"# split the dataset into a list of two seurat objects for each dataset seurat_obj.list <- SplitObject(seurat_obj, split.by = \"dataset_id\")  # normalize each dataset independently seurat_obj.list <- lapply(X = seurat_obj.list, FUN = function(x) {   x <- SCTransform(x) })  # select features for integration features <- SelectIntegrationFeatures(object.list = seurat_obj.list) seurat_obj.list <- PrepSCTIntegration(seurat_obj.list, anchor.features = features) seurat_obj.anchors <- FindIntegrationAnchors(object.list = seurat_obj.list, anchor.features = features, normalization.method = \"SCT\") seurat_obj.combined <- IntegrateData(anchorset = seurat_obj.anchors, normalization.method = \"SCT\") DefaultAssay(seurat_obj.combined) <- \"integrated\"  # Run the standard workflow for visualization and clustering seurat_obj.combined <- ScaleData(seurat_obj.combined, verbose = FALSE) seurat_obj.combined <- RunPCA(seurat_obj.combined, npcs = 30, verbose = FALSE) seurat_obj.combined <- RunUMAP(seurat_obj.combined, reduction = \"pca\", dims = 1:30) # By assay p1 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"assay\") p1 # By cell type p2 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"cell_type\") p2"},{"path":"/articles/comp_bio_data_integration.html","id":"integration-across-datasets-using-dataset_id-and-controlling-for-batch-using-donor_id","dir":"Articles","previous_headings":"Integration with Seurat > Data integration with Seurat","what":"Integration across datasets using dataset_id and controlling for batch using donor_id","title":"Integrating multi-dataset slices of data with Seurat","text":"Similar dataset_id, cells Census annotated donor_id. definition donor_id depends dataset left discretion data curators. However still rich information can used batch variable integration. donor_id guaranteed unique across cells Census, strongly recommend concatenating dataset_id donor_id use batch separator Seurat Now perform integration. inspect new results UMAP. Plot UMAP.   can see using dataset_id donor_id batch cells now mostly cluster cell type.","code":"# split the dataset into a list of two seurat objects for each dataset seurat_obj.list <- SplitObject(seurat_obj, split.by = \"dataset_id\")  # normalize each dataset independently controlling for batch seurat_obj.list <- lapply(X = seurat_obj.list, FUN = function(x) {   x <- SCTransform(x, vars.to.regress = \"donor_id\") })  # select features for integration features <- SelectIntegrationFeatures(object.list = seurat_obj.list) seurat_obj.list <- PrepSCTIntegration(seurat_obj.list, anchor.features = features) seurat_obj.anchors <- FindIntegrationAnchors(object.list = seurat_obj.list, anchor.features = features, normalization.method = \"SCT\") #> Finding all pairwise anchors #> Running CCA #> Merging objects #> Finding neighborhoods #> Finding anchors #>  Found 7136 anchors #> Filtering anchors #>  Retained 5048 anchors seurat_obj.combined <- IntegrateData(anchorset = seurat_obj.anchors, normalization.method = \"SCT\") #> [1] 1 #> Warning: Different cells and/or features from existing assay SCT #> [1] 2 #> Warning: Different cells and/or features from existing assay SCT #> Merging dataset 1 into 2 #> Extracting anchors for merged samples #> Finding integration vectors #> Finding integration vector weights #> Integrating data #> Warning: Assay integrated changing from Assay to SCTAssay  #> Warning: Different cells and/or features from existing assay SCT DefaultAssay(seurat_obj.combined) <- \"integrated\"  # Run the standard workflow for visualization and clustering seurat_obj.combined <- RunPCA(seurat_obj.combined, npcs = 30, verbose = FALSE) seurat_obj.combined <- RunUMAP(seurat_obj.combined, reduction = \"pca\", dims = 1:30) #> 17:32:58 UMAP embedding parameters a = 0.9922 b = 1.112 #> 17:32:58 Read 10153 rows and found 30 numeric columns #> 17:32:58 Using Annoy for neighbor search, n_neighbors = 30 #> 17:32:58 Building Annoy index with metric = cosine, n_trees = 50 #> 0%   10   20   30   40   50   60   70   80   90   100% #> [----|----|----|----|----|----|----|----|----|----| #> **************************************************| #> 17:32:59 Writing NN index file to temp file /tmp/Rtmp91c2fH/file1306c933fc5688 #> 17:32:59 Searching Annoy index using 1 thread, search_k = 3000 #> 17:33:02 Annoy recall = 100% #> 17:33:02 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 #> 17:33:03 Initializing from normalized Laplacian + noise (using RSpectra) #> 17:33:03 Commencing optimization for 200 epochs, with 409718 positive edges #> 17:33:07 Optimization finished # By assay p1 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"assay\") p1 # By cell type p2 <- DimPlot(seurat_obj.combined, reduction = \"umap\", group.by = \"cell_type\") p2"},{"path":"/articles/comp_bio_data_integration.html","id":"integration-across-datasets-using-dataset_id-and-controlling-for-batch-using-donor_id-assay_ontology_term_id-suspension_type-","dir":"Articles","previous_headings":"Integration with Seurat > Data integration with Seurat","what":"Integration across datasets using dataset_id and controlling for batch using donor_id + assay_ontology_term_id + suspension_type.","title":"Integrating multi-dataset slices of data with Seurat","text":"cases one dataset may contain multiple assay types /multiple suspension types (cell vs nucleus), important consider metadata batches. Therefore, comprehensive definition batch Census can accomplished combining cell metadata dataset_id, donor_id, assay_ontology_term_id suspension_type, latter encode EFO ids assay types. example, two datasets used contain cells one assay , one suspension type . Thus make difference include metadata part batch. implementation look line","code":"# EXAMPLE, DON'T RUN.  # split the dataset into a list of seurat objects for each dataset seurat_obj.list <- SplitObject(seurat_obj, split.by = \"dataset_id\")  # normalize each dataset independently controlling for batch seurat_obj.list <- lapply(X = seurat_obj.list, FUN = function(x) {   x <- SCTransform(x, vars.to.regress = c(\"donor_id\", \"assay_ontology_term_id\", \"suspension_type\")) })  # select features for integration features <- SelectIntegrationFeatures(object.list = seurat_obj.list)  # integrate seurat_obj.list <- PrepSCTIntegration(seurat_obj.list, anchor.features = features) seurat_obj.anchors <- FindIntegrationAnchors(object.list = seurat_obj.list, anchor.features = features, normalization.method = \"SCT\") seurat_obj.combined <- IntegrateData(anchorset = seurat_obj.anchors, normalization.method = \"SCT\")"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the census","title":"Normalizing full-length gene sequencing data","text":"First open Census: can learn cellxgene.census methods accessing corresponding documentation, example ?cellxgene.census::open_soma.","code":"library(\"Seurat\") census <- cellxgene.census::open_soma()"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"fetching-full-length-example-sequencing-data-smart-seq","dir":"Articles","previous_headings":"","what":"Fetching full-length example sequencing data (Smart-Seq)","title":"Normalizing full-length gene sequencing data","text":"Let’s get example data, case ’ll fetch cells relatively small dataset derived Smart-Seq2 technology performs full-length gene sequencing: Collection: Tabula Muris Senis Dataset: Liver - single-cell transcriptomic atlas characterizes ageing tissues mouse - Smart-seq2 Let’s first find dataset’s id using dataset table Census. Now can use id fetch data. Let’s make sure data contains Smart-Seq2 cells. Great! can see small dataset containing 2,859 cells. Now let’s proceed normalize gene lengths.","code":"liver_dataset <- as.data.frame(   census$get(\"census_info\")$get(\"datasets\")         $read(value_filter=\"dataset_title == 'Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2'\")         $concat() ) liver_dataset #>   soma_joinid                        collection_id    collection_name #> 1         525 0b9d8a04-bb9d-44da-aa27-705bb65b54eb Tabula Muris Senis #>              collection_doi                           dataset_id #> 1 10.1038/s41586-020-2496-1 4546e757-34d0-4d17-be06-538318925fcd #>                                                                                       dataset_title #> 1 Liver - A single-cell transcriptomic atlas characterizes ageing tissues in the mouse - Smart-seq2 #>                           dataset_h5ad_path dataset_total_cell_count #> 1 4546e757-34d0-4d17-be06-538318925fcd.h5ad                     2859 liver_dataset_id <- liver_dataset[1,\"dataset_id\"] liver_seurat <- cellxgene.census::get_seurat(   census,   organism = \"Mus musculus\",   obs_value_filter = paste0(\"dataset_id == '\", liver_dataset_id, \"'\") ) table(liver_seurat$assay) #>  #> Smart-seq2  #>       2859"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"normalizing-expression-to-account-for-gene-length","dir":"Articles","previous_headings":"","what":"Normalizing expression to account for gene length","title":"Normalizing full-length gene sequencing data","text":"default cellxgene_census::get_seurat() fetches genes Census. let’s first identify genes measured dataset subset Seurat obect include . goal can use “Dataset Presence Matrix” census$get(\"census_data\")$get(\"mus_musculus\")$ms$get(\"RNA\")$get(\"feature_dataset_presence_matrix\"). boolean matrix N x M N number datasets, M number genes Census, 1 entry indicates gene measured dataset. (Note Seurat objects transposed layout M x N.) Let’s get genes measured dataset. can see genes Census 17,992 measured dataset. Now let’s normalize genes gene length. can easily Census gene lengths included gene metadata feature_length. done! can now see real numbers instead integers.","code":"liver_seurat #> An object of class Seurat  #> 52392 features across 2859 samples within 1 assay  #> Active assay: RNA (52392 features, 0 variable features) #>  2 layers present: counts, data liver_dataset_joinid <- liver_dataset$soma_joinid[1] presence_matrix <- cellxgene.census::get_presence_matrix(census, \"Mus musculus\", \"RNA\") presence_matrix <- presence_matrix$take(liver_dataset_joinid) gene_presence <- as.vector(presence_matrix$get_one_based_matrix())  liver_seurat <- liver_seurat[gene_presence, ] liver_seurat #> An object of class Seurat  #> 17992 features across 2859 samples within 1 assay  #> Active assay: RNA (17992 features, 0 variable features) #>  2 layers present: counts, data GetAssayData(liver_seurat[1:5, 1:5], slot = \"data\") #> Warning: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0. #> ℹ Please use the `layer` argument instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated. #> 5 x 5 sparse Matrix of class \"dgCMatrix\" #>                    cell3530079 cell3530080 cell3530081 cell3530082 cell3530083 #> ENSMUSG00000025900           .           .           .           .           . #> ENSMUSG00000025902           .           .           .           .        2250 #> ENSMUSG00000033845           .         559        1969           .           . #> ENSMUSG00000025903           .           .           .           .           . #> ENSMUSG00000033813           .           .         828           1          54 gene_lengths <- liver_seurat$RNA@meta.features$feature_length liver_seurat <- SetAssayData(   liver_seurat,    new.data = sweep(GetAssayData(liver_seurat, slot = \"data\"), 1, gene_lengths, \"/\") ) GetAssayData(liver_seurat[1:5, 1:5], slot = \"data\") #> 5 x 5 sparse Matrix of class \"dgCMatrix\" #>                    cell3530079 cell3530080 cell3530081  cell3530082 cell3530083 #> ENSMUSG00000025900           .  .            .         .             .          #> ENSMUSG00000025902           .  .            .         .             0.47150042 #> ENSMUSG00000033845           .  0.06586544   0.2320019 .             .          #> ENSMUSG00000025903           .  .            .         .             .          #> ENSMUSG00000033813           .  .            0.2744448 0.0003314551  0.01789857"},{"path":"/articles/comp_bio_normalizing_full_gene_sequencing.html","id":"validation-through-clustering-exploration","dir":"Articles","previous_headings":"","what":"Validation through clustering exploration","title":"Normalizing full-length gene sequencing data","text":"Let’s perform basic clustering analysis see cell types cluster expected using normalized counts. First basic filtering cells genes. normalize account sequencing depth transform data log scale. subset highly variable genes. finally scale values across gene axis. Now can proceed clustering analysis.  exceptions can see cells cell type cluster near serves sanity check gene-length normalization applied. Don’t forget close census.","code":"cells_per_gene <- rowSums(GetAssayData(liver_seurat, slot = \"counts\") > 0) genes_per_cell <- Matrix::colSums(liver_seurat$RNA@counts > 0) liver_seurat <- liver_seurat[cells_per_gene >= 5, genes_per_cell >= 500] liver_seurat <- Seurat::NormalizeData(   liver_seurat,   normalization.method = \"LogNormalize\",   scale.factor = 10000 ) liver_seurat <- Seurat::FindVariableFeatures(   liver_seurat,   selection.method = \"vst\",   nfeatures = 1000 ) all.genes <- rownames(liver_seurat) liver_seurat <- Seurat::ScaleData(liver_seurat, features = all.genes) liver_seurat <- RunPCA(   liver_seurat,   features = VariableFeatures(object = liver_seurat) ) liver_seurat <- FindNeighbors(liver_seurat, dims = 1:40) liver_seurat <- RunUMAP(liver_seurat, dims = 1:40) DimPlot(liver_seurat, reduction = \"umap\", group.by = \"cell_type\") census$close()"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"opening-the-census","dir":"Articles","previous_headings":"","what":"Opening the Census","title":"Summarizing cell and gene metadata","text":"cellxgene.census R package contains convenient API open version Census (default, newest stable version). open Census, close census$close(). can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma(). can learn cellxgene.census methods accessing corresponding documentation. example ?cellxgene.census::open_soma.","code":"library(\"cellxgene.census\") census <- open_soma()"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"summarizing-cell-metadata","dir":"Articles","previous_headings":"","what":"Summarizing cell metadata","title":"Summarizing cell and gene metadata","text":"Census open can use TileDB-SOMA methods SOMACollection. can thus access metadata SOMADataFrame objects encoding cell gene metadata. Tips: can read entire SOMADataFrame R using .data.frame(soma_df$read()$concat()). Queries much faster request DataFrame columns required analysis (e.g. column_names = c(\"soma_joinid\", \"cell_type_ontology_term_id\")). can also refine query results using value_filter, filter census matching records.","code":""},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"example-summarize-all-cell-types","dir":"Articles","previous_headings":"Summarizing cell metadata","what":"Example: Summarize all cell types","title":"Summarizing cell and gene metadata","text":"example reads cell metadata (obs) R data frame summarize variety ways.","code":"human <- census$get(\"census_data\")$get(\"homo_sapiens\")  # Read obs into an R data frame (tibble). obs_df <- human$obs$read(column_names = c(\"cell_type\")) obs_df <- as.data.frame(obs_df$concat())  # Find all unique values in the cell_type column. unique_cell_type <- unique(obs_df$cell_type)  cat(   \"There are\",   length(unique_cell_type),   \"cell types in the Census! The first few are: \",   paste(head(unique_cell_type), collapse = \", \") ) #> There are 613 cell types in the Census! The first few are:  syncytiotrophoblast cell, placental villous trophoblast, extravillous trophoblast, trophoblast giant cell, decidual natural killer cell, human, T cell"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"example-summarize-a-subset-of-cell-types-selected-with-a-value_filter","dir":"Articles","previous_headings":"Summarizing cell metadata","what":"Example: Summarize a subset of cell types, selected with a value_filter","title":"Summarizing cell and gene metadata","text":"example utilizes SOMA “value filter” read subset cells tissue_ontology_term_id equal UBERON:0002048 (lung tissue), summarizes query result. can also define much complex value filters. example: combine terms & | use %% operator query multiple values","code":"# Read cell_type terms for cells which have a specific tissue term LUNG_TISSUE <- \"UBERON:0002048\"  obs_df <- human$obs$read(column_names = c(\"cell_type\"), value_filter = paste0(\"tissue_ontology_term_id == '\", LUNG_TISSUE, \"'\")) obs_df <- as.data.frame(obs_df$concat())  # Find all unique values in the cell_type column as an R data frame. unique_cell_type <- unique(obs_df$cell_type) cat(   \"There are \",   length(unique_cell_type),   \" cell types in the Census where tissue_ontology_term_id == \",   LUNG_TISSUE,   \"!\\nThe first few are:\",   paste(head(unique_cell_type), collapse = \", \"),   \"\\n\" ) #> There are  185  cell types in the Census where tissue_ontology_term_id ==  UBERON:0002048 ! #> The first few are: type II pneumocyte, neutrophil, effector CD4-positive, alpha-beta T cell, effector CD8-positive, alpha-beta T cell, mature NK T cell, blood vessel endothelial cell  # Report the 10 most common top_10 <- sort(table(obs_df$cell_type), decreasing = TRUE)[1:10] cat(   \"The top 10 cell types where tissue_ontology_term_id ==\",    LUNG_TISSUE,   \"are: \",   paste(names(top_10), collapse = \", \") ) #> The top 10 cell types where tissue_ontology_term_id == UBERON:0002048 are:  native cell, alveolar macrophage, CD8-positive, alpha-beta T cell, CD4-positive, alpha-beta T cell, macrophage, type II pneumocyte, classical monocyte, natural killer cell, malignant cell, epithelial cell of lower respiratory tract # You can also do more complex queries, such as testing for inclusion in a list of values obs_df <- human$obs$read(   column_names = c(\"cell_type_ontology_term_id\"),   value_filter = \"tissue_ontology_term_id %in% c('UBERON:0002082', 'UBERON:OOO2084', 'UBERON:0002080')\" )  obs_df <- as.data.frame(obs_df$concat())  # Summarize top_10 <- sort(table(obs_df$cell_type_ontology_term_id), decreasing = TRUE)[1:10] print(top_10) #>  #> CL:0000746 CL:0008034 CL:0002548 CL:0000115 CL:0002131 CL:0000763 CL:0000669 CL:0000003  #>     159096      84750      79618      64190      61830      32088      27515      22707  #> CL:0000057 CL:0002144  #>      20117      18593"},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"full-census-metadata-stats","dir":"Articles","previous_headings":"","what":"Full Census metadata stats","title":"Summarizing cell and gene metadata","text":"example queries organisms Census, summarizes diversity various metadata labels.","code":"cols_to_query <- c(   \"cell_type_ontology_term_id\",   \"assay_ontology_term_id\",   \"tissue_ontology_term_id\" )  total_cells <- 0 for (organism in census$get(\"census_data\")$names()) {   print(organism)      obs_df <- census$get(\"census_data\")$get(organism)$obs$read(column_names = cols_to_query)   obs_df <- as.data.frame(obs_df$concat())      total_cells <- total_cells + nrow(obs_df)   for (col in cols_to_query) {     cat(\"  Unique \", col, \" values: \", length(unique(obs_df[[col]])), \"\\n\")   } } #> [1] \"mus_musculus\" #>   Unique  cell_type_ontology_term_id  values:  248  #>   Unique  assay_ontology_term_id  values:  9  #>   Unique  tissue_ontology_term_id  values:  66  #> [1] \"homo_sapiens\" #>   Unique  cell_type_ontology_term_id  values:  613  #>   Unique  assay_ontology_term_id  values:  19  #>   Unique  tissue_ontology_term_id  values:  220 cat(\"Complete Census contains \", total_cells, \" cells.\") #> Complete Census contains  61656118  cells."},{"path":"/articles/comp_bio_summarize_axis_query.html","id":"close-the-census","dir":"Articles","previous_headings":"Full Census metadata stats","what":"Close the census","title":"Summarizing cell and gene metadata","text":"use, census object closed release memory resources. also closes SOMA objects accessed via top-level census. Closing can automated using .exit(census$close(), add = TRUE) immediately census <- open_soma().","code":"census$close()"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Chan Zuckerberg Initiative Foundation. Author, maintainer, copyright holder, funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Chan Zuckerberg Initiative Foundation (2023). cellxgene.census: CZ CELLxGENE Discover Cell Census. R package version 1.8.0, https://github.com/chanzuckerberg/cellxgene-census.","code":"@Manual{,   title = {cellxgene.census: CZ CELLxGENE Discover Cell Census},   author = {{Chan Zuckerberg Initiative Foundation}},   year = {2023},   note = {R package version 1.8.0},   url = {https://github.com/chanzuckerberg/cellxgene-census}, }"},{"path":"/index.html","id":"r-package-of-cz-cellxgene-discover-census","dir":"","previous_headings":"","what":"CZ CELLxGENE Discover Cell Census","title":"CZ CELLxGENE Discover Cell Census","text":"documentation R package cellxgene.census part CZ CELLxGENE Discover Census. full details Census data capabilities please go main Census site. cellxgene.census provides API efficiently access cloud-hosted Census single-cell data R. just seconds users can access slice Census data using cell gene filters across hundreds single-cell datasets. Census data can fetched iterative fashion bigger--memory slices data, quickly exported basic R structures, well Seurat SingleCellExperiment objects downstream analysis.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"CZ CELLxGENE Discover Cell Census","text":"installing Ubuntu, may need install following libraries via apt install, libxml2-dev libssl-dev libcurl4-openssl-dev. addition must cmake v3.21 greater. installing MacOS, need install developer tools Xcode. Windows supported. R session install cellxgene.census R-Universe. able export Census data Seurat SingleCellExperiment also need install respective packages.","code":"install.packages(   \"cellxgene.census\",   repos=c('https://chanzuckerberg.r-universe.dev', 'https://cloud.r-project.org') ) # Seurat install.packages(\"Seurat\")  # SingleCellExperiment if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"SingleCellExperiment\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"CZ CELLxGENE Discover Cell Census","text":"Check vignettes “Articles” section navigation bar site. highly recommend following vignettes starting point: Querying fetching single-cell data cell/gene metadata Learning CZ CELLxGENE Discover Census can also check quick start guide main Census site.","code":""},{"path":"/index.html","id":"example-seurat-and-singlecellexperiment-query","dir":"","previous_headings":"Usage","what":"Example Seurat and SingleCellExperiment query","title":"CZ CELLxGENE Discover Cell Census","text":"following creates Seurat object -demand sympathetic neurons Census filtering genes ENSG00000161798, ENSG00000188229. following retrieves data SingleCellExperiment object.","code":"library(\"cellxgene.census\") library(\"Seurat\")  census <- open_soma()  organism <- \"Homo sapiens\" gene_filter <- \"feature_id %in% c('ENSG00000107317', 'ENSG00000106034')\" cell_filter <-  \"cell_type == 'sympathetic neuron'\" cell_columns <- c(\"assay\", \"cell_type\", \"tissue\", \"tissue_general\", \"suspension_type\", \"disease\")  seurat_obj <- get_seurat(    census = census,    organism = organism,    var_value_filter = gene_filter,    obs_value_filter = cell_filter,    obs_column_names = cell_columns ) library(\"SingleCellExperiment\")  sce_obj <- get_single_cell_experiment(    census = census,    organism = organism,    var_value_filter = gene_filter,    obs_value_filter = cell_filter,    obs_column_names = cell_columns )"},{"path":"/index.html","id":"for-more-help","dir":"","previous_headings":"","what":"For More Help","title":"CZ CELLxGENE Discover Cell Census","text":"help, please go visit main Census site. believe found security issue, appreciate notification. Please send email security@chanzuckerberg.com.","code":""},{"path":"/reference/download_source_h5ad.html","id":null,"dir":"Reference","previous_headings":"","what":"Download source H5AD to local file name. — download_source_h5ad","title":"Download source H5AD to local file name. — download_source_h5ad","text":"Download source H5AD local file name.","code":""},{"path":"/reference/download_source_h5ad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download source H5AD to local file name. — download_source_h5ad","text":"","code":"download_source_h5ad(   dataset_id,   file,   overwrite = FALSE,   census_version = \"stable\",   census = NULL )"},{"path":"/reference/download_source_h5ad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download source H5AD to local file name. — download_source_h5ad","text":"dataset_id dataset_id interest. file Local file name store H5AD file. overwrite TRUE allow overwriting existing file. census_version desired Census version. census open Census handle census_version. provided, opened closed automatically; efficient reuse handle calling download_source_h5ad() multiple times.","code":""},{"path":"/reference/download_source_h5ad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download source H5AD to local file name. — download_source_h5ad","text":"","code":"download_source_h5ad(\"0895c838-e550-48a3-a777-dbcd35d30272\", \"/tmp/data.h5ad\", overwrite = TRUE)"},{"path":"/reference/get_census_version_description.html","id":null,"dir":"Reference","previous_headings":"","what":"Get release description for a Census version — get_census_version_description","title":"Get release description for a Census version — get_census_version_description","text":"Get release description Census version","code":""},{"path":"/reference/get_census_version_description.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get release description for a Census version — get_census_version_description","text":"","code":"get_census_version_description(census_version)"},{"path":"/reference/get_census_version_description.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get release description for a Census version — get_census_version_description","text":"census_version census version name.","code":""},{"path":"/reference/get_census_version_description.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get release description for a Census version — get_census_version_description","text":"List release location metadata","code":""},{"path":"/reference/get_census_version_description.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get release description for a Census version — get_census_version_description","text":"","code":"as.data.frame(get_census_version_description(\"stable\")) #>   release_date release_build #> 1                 2023-07-25 #>                                                  soma.uri #> 1 s3://cellxgene-data-public/cell-census/2023-07-25/soma/ #>               soma.relative_uri soma.s3_region #> 1 /cell-census/2023-07-25/soma/      us-west-2 #>                                                  h5ads.uri #> 1 s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/ #>               h5ads.relative_uri h5ads.s3_region do_not_delete  lts  alias #> 1 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE TRUE stable #>   census_version #> 1         stable"},{"path":"/reference/get_census_version_directory.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the directory of Census releases currently available — get_census_version_directory","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"Get directory Census releases currently available","code":""},{"path":"/reference/get_census_version_directory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"","code":"get_census_version_directory()"},{"path":"/reference/get_census_version_directory.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"Data frame available cell census releases, including location metadata.","code":""},{"path":"/reference/get_census_version_directory.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the directory of Census releases currently available — get_census_version_directory","text":"","code":"get_census_version_directory() #>            release_date release_build #> stable                     2023-07-25 #> latest                     2023-11-13 #> 2023-05-15                 2023-05-15 #> 2023-07-25                 2023-07-25 #> 2023-10-18                 2023-10-18 #> 2023-10-23                 2023-10-23 #> 2023-10-30                 2023-10-30 #> 2023-11-06                 2023-11-06 #> 2023-11-13                 2023-11-13 #>                                                           soma.uri #> stable     s3://cellxgene-data-public/cell-census/2023-07-25/soma/ #> latest     s3://cellxgene-data-public/cell-census/2023-11-13/soma/ #> 2023-05-15 s3://cellxgene-data-public/cell-census/2023-05-15/soma/ #> 2023-07-25 s3://cellxgene-data-public/cell-census/2023-07-25/soma/ #> 2023-10-18 s3://cellxgene-data-public/cell-census/2023-10-18/soma/ #> 2023-10-23 s3://cellxgene-data-public/cell-census/2023-10-23/soma/ #> 2023-10-30 s3://cellxgene-data-public/cell-census/2023-10-30/soma/ #> 2023-11-06 s3://cellxgene-data-public/cell-census/2023-11-06/soma/ #> 2023-11-13 s3://cellxgene-data-public/cell-census/2023-11-13/soma/ #>                        soma.relative_uri soma.s3_region #> stable     /cell-census/2023-07-25/soma/      us-west-2 #> latest     /cell-census/2023-11-13/soma/      us-west-2 #> 2023-05-15 /cell-census/2023-05-15/soma/      us-west-2 #> 2023-07-25 /cell-census/2023-07-25/soma/      us-west-2 #> 2023-10-18 /cell-census/2023-10-18/soma/      us-west-2 #> 2023-10-23 /cell-census/2023-10-23/soma/      us-west-2 #> 2023-10-30 /cell-census/2023-10-30/soma/      us-west-2 #> 2023-11-06 /cell-census/2023-11-06/soma/      us-west-2 #> 2023-11-13 /cell-census/2023-11-13/soma/      us-west-2 #>                                                           h5ads.uri #> stable     s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/ #> latest     s3://cellxgene-data-public/cell-census/2023-11-13/h5ads/ #> 2023-05-15 s3://cellxgene-data-public/cell-census/2023-05-15/h5ads/ #> 2023-07-25 s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/ #> 2023-10-18 s3://cellxgene-data-public/cell-census/2023-10-18/h5ads/ #> 2023-10-23 s3://cellxgene-data-public/cell-census/2023-10-23/h5ads/ #> 2023-10-30 s3://cellxgene-data-public/cell-census/2023-10-30/h5ads/ #> 2023-11-06 s3://cellxgene-data-public/cell-census/2023-11-06/h5ads/ #> 2023-11-13 s3://cellxgene-data-public/cell-census/2023-11-13/h5ads/ #>                        h5ads.relative_uri h5ads.s3_region do_not_delete   lts #> stable     /cell-census/2023-07-25/h5ads/       us-west-2          TRUE  TRUE #> latest     /cell-census/2023-11-13/h5ads/       us-west-2         FALSE    NA #> 2023-05-15 /cell-census/2023-05-15/h5ads/       us-west-2          TRUE  TRUE #> 2023-07-25 /cell-census/2023-07-25/h5ads/       us-west-2          TRUE  TRUE #> 2023-10-18 /cell-census/2023-10-18/h5ads/       us-west-2         FALSE FALSE #> 2023-10-23 /cell-census/2023-10-23/h5ads/       us-west-2          TRUE FALSE #> 2023-10-30 /cell-census/2023-10-30/h5ads/       us-west-2         FALSE FALSE #> 2023-11-06 /cell-census/2023-11-06/h5ads/       us-west-2         FALSE    NA #> 2023-11-13 /cell-census/2023-11-13/h5ads/       us-west-2         FALSE    NA #>             alias #> stable     stable #> latest     latest #> 2023-05-15        #> 2023-07-25        #> 2023-10-18        #> 2023-10-23        #> 2023-10-30        #> 2023-11-06        #> 2023-11-13"},{"path":"/reference/get_presence_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"Read the feature dataset presence matrix. — get_presence_matrix","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"Read feature dataset presence matrix.","code":""},{"path":"/reference/get_presence_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"","code":"get_presence_matrix(census, organism, measurement_name = \"RNA\")"},{"path":"/reference/get_presence_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"census census object cellxgene.census::open_soma(). organism organism query, usually one Homo sapiens Mus musculus measurement_name measurement object query. Defaults RNA.","code":""},{"path":"/reference/get_presence_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"tiledbsoma::matrixZeroBasedView object dataset join id & feature join id dimensions, filled 1s indicating presence. sparse matrix accessed zero-based indexes since join id's may zero.","code":""},{"path":"/reference/get_presence_matrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read the feature dataset presence matrix. — get_presence_matrix","text":"","code":"census <- open_soma() #> The stable Census release is currently 2023-07-25. Specify census_version = \"2023-07-25\" in future calls to open_soma() to ensure data consistency. on.exit(census$close(), add = TRUE) print(get_presence_matrix(census, \"Homo sapiens\")$dim()) #> Error in private$check_open_for_read_or_write(): Item must be open for read or write. s3://cellxgene-data-public/cell-census/2023-07-25/soma/"},{"path":"/reference/get_seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Census slices to Seurat — get_seurat","title":"Export Census slices to Seurat — get_seurat","text":"Convenience wrapper around SOMAExperimentAxisQuery, build execute query, return Seurat object.","code":""},{"path":"/reference/get_seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Census slices to Seurat — get_seurat","text":"","code":"get_seurat(   census,   organism,   measurement_name = \"RNA\",   X_layers = c(counts = \"raw\", data = NULL),   obs_value_filter = NULL,   obs_coords = NULL,   obs_column_names = NULL,   var_value_filter = NULL,   var_coords = NULL,   var_column_names = NULL,   var_index = \"feature_id\" )"},{"path":"/reference/get_seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Census slices to Seurat — get_seurat","text":"census census object, usually returned cellxgene.census::open_soma(). organism organism query, usually one Homo sapiens Mus musculus measurement_name measurement object query. Defaults RNA. X_layers named character X layers add Seurat assay, names names Seurat slots (counts data) values names layers within X. obs_value_filter SOMA value_filter across columns obs dataframe, expressed string. obs_coords set coordinates obs dataframe index, expressed type format supported SOMADataFrame's read() method. obs_column_names Columns fetch obs data frame. var_value_filter obs_value_filter var. var_coords obs_coords var. var_column_names Columns fetch var data frame. var_index Name column ‘var’ add feature names.","code":""},{"path":"/reference/get_seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export Census slices to Seurat — get_seurat","text":"Seurat object containing sensus slice.","code":""},{"path":"/reference/get_seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Export Census slices to Seurat — get_seurat","text":"","code":"if (FALSE) { census <- open_soma() seurat_obj <- get_seurat(   census,   organism = \"Homo sapiens\",   obs_value_filter = \"cell_type == 'leptomeningeal cell'\",   var_value_filter = \"feature_id %in% c('ENSG00000107317', 'ENSG00000106034')\" )  seurat_obj  census$close() }"},{"path":"/reference/get_single_cell_experiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"Convenience wrapper around SOMAExperimentAxisQuery, build execute query, return SingleCellExperiment object.","code":""},{"path":"/reference/get_single_cell_experiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"","code":"get_single_cell_experiment(   census,   organism,   measurement_name = \"RNA\",   X_layers = c(counts = \"raw\"),   obs_value_filter = NULL,   obs_coords = NULL,   obs_column_names = NULL,   var_value_filter = NULL,   var_coords = NULL,   var_column_names = NULL,   var_index = \"feature_id\" )"},{"path":"/reference/get_single_cell_experiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"census census object, usually returned cellxgene.census::open_soma(). organism organism query, usually one Homo sapiens Mus musculus measurement_name measurement object query. Defaults RNA. X_layers character vector X layers add assays main experiment; may optionally named set name resulting assay (eg. ‘X_layers = c(counts = \"raw\")’ load X layer “‘raw’” assay “‘counts’”); default, loads X layers obs_value_filter SOMA value_filter across columns obs dataframe, expressed string. obs_coords set coordinates obs dataframe index, expressed type format supported SOMADataFrame's read() method. obs_column_names Columns fetch obs data frame. var_value_filter obs_value_filter var. var_coords obs_coords var. var_column_names Columns fetch var data frame. var_index Name column ‘var’ add feature names.","code":""},{"path":"/reference/get_single_cell_experiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"SingleCellExperiment object containing sensus slice.","code":""},{"path":"/reference/get_single_cell_experiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Export Census slices to SingleCellExperiment — get_single_cell_experiment","text":"","code":"if (FALSE) { census <- open_soma() sce_obj <- get_single_cell_experiment(   census,   organism = \"Homo sapiens\",   obs_value_filter = \"cell_type == 'leptomeningeal cell'\",   var_value_filter = \"feature_id %in% c('ENSG00000107317', 'ENSG00000106034')\" )  sce_obj  census$close() }"},{"path":"/reference/get_source_h5ad_uri.html","id":null,"dir":"Reference","previous_headings":"","what":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"Locate source h5ad file dataset.","code":""},{"path":"/reference/get_source_h5ad_uri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"","code":"get_source_h5ad_uri(dataset_id, census_version = \"stable\", census = NULL)"},{"path":"/reference/get_source_h5ad_uri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"dataset_id dataset_id interest. census_version desired Census version. census open Census handle census_version. provided, opened closed automatically; efficient reuse handle calling get_source_h5ad_uri() multiple times.","code":""},{"path":"/reference/get_source_h5ad_uri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"list uri optional s3_region.","code":""},{"path":"/reference/get_source_h5ad_uri.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Locate source h5ad file for a dataset. — get_source_h5ad_uri","text":"","code":"get_source_h5ad_uri(\"0895c838-e550-48a3-a777-dbcd35d30272\") #> $uri #> [1] \"s3://cellxgene-data-public/cell-census/2023-07-25/h5ads/0895c838-e550-48a3-a777-dbcd35d30272.h5ad\" #>  #> $s3_region #> [1] \"us-west-2\" #>"},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":null,"dir":"Reference","previous_headings":"","what":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"Create SOMATileDBContext suitable using open_soma(). Typically open_soma() creates context automatically, one can created separately order set custom configuration options, share multiple open Census handles.","code":""},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"","code":"new_SOMATileDBContext_for_census(census_version_description, ...)"},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"census_version_description result get_census_version_description() desired Census version. ... Custom configuration options.","code":""},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"SOMATileDBContext object open_soma().","code":""},{"path":"/reference/new_SOMATileDBContext_for_census.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create SOMATileDBContext for Census — new_SOMATileDBContext_for_census","text":"","code":"census_desc <- get_census_version_description(\"stable\") ctx <- new_SOMATileDBContext_for_census(census_desc, \"soma.init_buffer_bytes\" = paste(4 * 1024**3)) census <- open_soma(\"stable\", tiledbsoma_ctx = ctx) #> The stable Census release is currently 2023-07-25. Specify census_version = \"2023-07-25\" in future calls to open_soma() to ensure data consistency. census$close()"},{"path":"/reference/open_soma.html","id":null,"dir":"Reference","previous_headings":"","what":"Open the Census — open_soma","title":"Open the Census — open_soma","text":"Open Census","code":""},{"path":"/reference/open_soma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Open the Census — open_soma","text":"","code":"open_soma(census_version = \"stable\", uri = NULL, tiledbsoma_ctx = NULL)"},{"path":"/reference/open_soma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Open the Census — open_soma","text":"census_version version Census, e.g., \"stable\". uri URI containing Census SOMA objects open instead released version. (supplied, takes precedence census_version.) tiledbsoma_ctx tiledbsoma::SOMATileDBContext built using new_SOMATileDBContext_for_census(). Optional (created automatically) using census_version context need reused.","code":""},{"path":"/reference/open_soma.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Open the Census — open_soma","text":"Top-level tiledbsoma::SOMACollection object. use, census closed release memory resources, usually .exit(census$close(), add = TRUE). Closing top-level census also close SOMA objects accessed .","code":""},{"path":"/reference/open_soma.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Open the Census — open_soma","text":"","code":"census <- open_soma() #> The stable Census release is currently 2023-07-25. Specify census_version = \"2023-07-25\" in future calls to open_soma() to ensure data consistency. as.data.frame(census$get(\"census_info\")$get(\"summary\")$read()$concat()) #>   soma_joinid                      label      value #> 1           0      census_schema_version      1.0.0 #> 2           1          census_build_date 2023-07-25 #> 3           2     dataset_schema_version      3.0.0 #> 4           3           total_cell_count   61656118 #> 5           4          unique_cell_count   37447773 #> 6           5 number_donors_homo_sapiens      13035 #> 7           6 number_donors_mus_musculus       1417 census$close()"}]
diff --git a/api/r/cellxgene.census/tests/README.md b/api/r/cellxgene.census/tests/README.md
index c6e638f80..eddb2aa34 100644
--- a/api/r/cellxgene.census/tests/README.md
+++ b/api/r/cellxgene.census/tests/README.md
@@ -42,6 +42,226 @@ When run, please record the results in this file (below) and commit the change t
   - `stdout.txt`
   - `acceptance-tests-logs-[YYY]-[MM]-[DD].csv`
 
+### 2023-10-23
+
+- Host: EC2 instance type: `r6id.x32xlarge`, all nvme mounted as swap.
+- Uname: Linux 6.2.0-1015-aws #15~22.04.1-Ubuntu SMP Fri Oct  6 21:37:24 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux
+- Census version
+
+```r
+> cellxgene.census::get_census_version_description('2023-10-23')
+$release_date
+[1] ""
+
+$release_build
+[1] "2023-10-23"
+
+$soma.uri
+[1] "s3://cellxgene-data-public/cell-census/2023-10-23/soma/"
+
+$soma.relative_uri
+[1] "/cell-census/2023-10-23/soma/"
+
+$soma.s3_region
+[1] "us-west-2"
+
+$h5ads.uri
+[1] "s3://cellxgene-data-public/cell-census/2023-10-23/h5ads/"
+
+$h5ads.relative_uri
+[1] "/cell-census/2023-10-23/h5ads/"
+
+$h5ads.s3_region
+[1] "us-west-2"
+
+$do_not_delete
+[1] TRUE
+
+$lts
+[1] FALSE
+
+$alias
+[1] ""
+
+$census_version
+[1] "2023-10-23"
+```
+
+- R session info
+
+```r
+> library("cellxgene.census"); sessionInfo()
+R version 4.3.2 (2023-10-31)
+Platform: x86_64-pc-linux-gnu (64-bit)
+Running under: Ubuntu 22.04.3 LTS
+
+Matrix products: default
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+
+locale:
+ [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
+ [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
+ [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
+[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
+
+time zone: Etc/UTC
+tzcode source: system (glibc)
+
+attached base packages:
+[1] stats     graphics  grDevices utils     datasets  methods   base
+
+other attached packages:
+[1] cellxgene.census_1.7.0
+
+loaded via a namespace (and not attached):
+ [1] Matrix_1.6-2        bit_4.0.5           jsonlite_1.8.7
+ [4] dplyr_1.1.3         compiler_4.3.2      tidyselect_1.2.0
+ [7] Rcpp_1.0.11         xml2_1.3.5          urltools_1.7.3
+[10] assertthat_0.2.1    tiledbsoma_1.5.1    arrow_13.0.0.1
+[13] triebeard_0.4.1     lattice_0.22-5      R6_2.5.1
+[16] generics_0.1.3      curl_5.1.0          RcppSpdlog_0.0.14
+[19] tibble_3.2.1        aws.s3_0.3.21       pillar_1.9.0
+[22] rlang_1.1.2         utf8_1.2.4          fs_1.6.3
+[25] bit64_4.0.5         cli_3.6.1           magrittr_2.0.3
+[28] spdl_0.0.5          digest_0.6.33       grid_4.3.2
+[31] base64enc_0.1-3     lifecycle_1.0.4     vctrs_0.6.4
+[34] glue_1.6.2          data.table_1.14.8   aws.signature_0.6.0
+[37] fansi_1.0.5         purrr_1.0.2         httr_1.4.7
+[40] tools_4.3.2         pkgconfig_2.0.3
+```
+
+- `stdout.txt`
+
+```text
+START TEST ( 2023-11-14 22:08:16.174153 ):  test_load_obs_human
+END TEST ( 2023-11-14 22:08:22.98588 ):  test_load_obs_human
+START TEST ( 2023-11-14 22:08:22.98783 ):  test_load_var_human
+END TEST ( 2023-11-14 22:08:25.442495 ):  test_load_var_human
+START TEST ( 2023-11-14 22:08:25.444479 ):  test_load_obs_mouse
+END TEST ( 2023-11-14 22:08:28.024443 ):  test_load_obs_mouse
+START TEST ( 2023-11-14 22:08:28.026742 ):  test_load_var_mouse
+END TEST ( 2023-11-14 22:08:30.551841 ):  test_load_var_mouse
+START TEST ( 2023-11-14 22:08:30.553875 ):  test_incremental_read_obs_human
+END TEST ( 2023-11-14 22:08:34.939039 ):  test_incremental_read_obs_human
+START TEST ( 2023-11-14 22:08:34.941016 ):  test_incremental_read_var_human
+END TEST ( 2023-11-14 22:08:37.237972 ):  test_incremental_read_var_human
+START TEST ( 2023-11-14 22:08:37.240094 ):  test_incremental_read_obs_mouse
+END TEST ( 2023-11-14 22:08:39.94039 ):  test_incremental_read_obs_mouse
+START TEST ( 2023-11-14 22:08:39.942593 ):  test_incremental_read_var_mouse
+END TEST ( 2023-11-14 22:08:42.370465 ):  test_incremental_read_var_mouse
+START TEST ( 2023-11-14 22:08:42.372389 ):  test_incremental_read_X_human
+END TEST ( 2023-11-14 22:34:22.488574 ):  test_incremental_read_X_human
+START TEST ( 2023-11-14 22:34:22.492159 ):  test_incremental_read_X_human-large-buffer-size
+END TEST ( 2023-11-14 23:09:18.232126 ):  test_incremental_read_X_human-large-buffer-size
+START TEST ( 2023-11-14 23:09:18.264509 ):  test_incremental_read_X_mouse
+END TEST ( 2023-11-14 23:11:56.351876 ):  test_incremental_read_X_mouse
+START TEST ( 2023-11-14 23:11:56.353827 ):  test_incremental_read_X_mouse-large-buffer-size
+END TEST ( 2023-11-14 23:14:17.630646 ):  test_incremental_read_X_mouse-large-buffer-size
+START TEST ( 2023-11-14 23:14:17.632895 ):  test_incremental_query_human_brain
+END TEST ( 2023-11-14 23:15:55.335929 ):  test_incremental_query_human_brain
+START TEST ( 2023-11-14 23:15:55.338135 ):  test_incremental_query_human_aorta
+END TEST ( 2023-11-14 23:16:08.916269 ):  test_incremental_query_human_aorta
+START TEST ( 2023-11-14 23:16:08.918454 ):  test_incremental_query_mouse_brain
+END TEST ( 2023-11-14 23:16:37.376596 ):  test_incremental_query_mouse_brain
+START TEST ( 2023-11-14 23:16:37.379342 ):  test_incremental_query_mouse_aorta
+END TEST ( 2023-11-14 23:16:47.94446 ):  test_incremental_query_mouse_aorta
+START TEST ( 2023-11-14 23:16:47.947125 ):  test_seurat_small-query
+END TEST ( 2023-11-14 23:17:14.07548 ):  test_seurat_small-query
+START TEST ( 2023-11-14 23:17:14.077846 ):  test_seurat_10K-cells-human
+END TEST ( 2023-11-14 23:17:38.149094 ):  test_seurat_10K-cells-human
+START TEST ( 2023-11-14 23:17:38.15124 ):  test_seurat_100K-cells-human
+END TEST ( 2023-11-14 23:19:20.828073 ):  test_seurat_100K-cells-human
+START TEST ( 2023-11-14 23:19:20.830142 ):  test_seurat_250K-cells-human
+END TEST ( 2023-11-14 23:23:40.847132 ):  test_seurat_250K-cells-human
+START TEST ( 2023-11-14 23:23:40.84913 ):  test_seurat_500K-cells-human
+END TEST ( 2023-11-14 23:25:15.641259 ):  test_seurat_500K-cells-human
+START TEST ( 2023-11-14 23:25:15.643847 ):  test_seurat_750K-cells-human
+END TEST ( 2023-11-14 23:27:27.224292 ):  test_seurat_750K-cells-human
+START TEST ( 2023-11-14 23:27:27.226668 ):  test_seurat_1M-cells-human
+END TEST ( 2023-11-14 23:30:22.724453 ):  test_seurat_1M-cells-human
+START TEST ( 2023-11-14 23:30:22.726397 ):  test_seurat_common-tissue
+END TEST ( 2023-11-14 23:36:58.342319 ):  test_seurat_common-tissue
+START TEST ( 2023-11-14 23:36:58.344535 ):  test_seurat_common-tissue-large-buffer-size
+END TEST ( 2023-11-14 23:43:24.081703 ):  test_seurat_common-tissue-large-buffer-size
+START TEST ( 2023-11-14 23:43:24.084217 ):  test_seurat_common-cell-type
+END TEST ( 2023-11-15 00:20:01.464067 ):  test_seurat_common-cell-type
+START TEST ( 2023-11-15 00:20:01.467048 ):  test_seurat_common-cell-type-large-buffer-size
+END TEST ( 2023-11-15 01:22:22.319294 ):  test_seurat_common-cell-type-large-buffer-size
+START TEST ( 2023-11-15 01:22:22.324062 ):  test_seurat_whole-enchilada-large-buffer-size
+END TEST ( 2023-11-15 01:22:22.331545 ):  test_seurat_whole-enchilada-large-buffer-size
+START TEST ( 2023-11-15 01:22:22.333495 ):  test_sce_small-query
+END TEST ( 2023-11-15 01:22:48.192984 ):  test_sce_small-query
+START TEST ( 2023-11-15 01:22:48.195162 ):  test_sce_10K-cells-human
+END TEST ( 2023-11-15 01:23:07.706087 ):  test_sce_10K-cells-human
+START TEST ( 2023-11-15 01:23:07.708716 ):  test_sce_100K-cells-human
+END TEST ( 2023-11-15 01:24:11.72169 ):  test_sce_100K-cells-human
+START TEST ( 2023-11-15 01:24:11.724583 ):  test_sce_250K-cells-human
+END TEST ( 2023-11-15 01:26:35.928612 ):  test_sce_250K-cells-human
+START TEST ( 2023-11-15 01:26:35.933126 ):  test_sce_500K-cells-human
+END TEST ( 2023-11-15 01:32:20.991126 ):  test_sce_500K-cells-human
+START TEST ( 2023-11-15 01:32:20.994043 ):  test_sce_750K-cells-human
+END TEST ( 2023-11-15 01:39:21.034239 ):  test_sce_750K-cells-human
+START TEST ( 2023-11-15 01:39:21.036942 ):  test_sce_1M-cells-human
+END TEST ( 2023-11-15 01:48:36.249403 ):  test_sce_1M-cells-human
+START TEST ( 2023-11-15 01:48:36.252596 ):  test_sce_common-tissue
+END TEST ( 2023-11-15 01:51:28.563483 ):  test_sce_common-tissue
+START TEST ( 2023-11-15 01:51:28.567205 ):  test_sce_common-tissue-large-buffer-size
+END TEST ( 2023-11-15 01:54:16.310687 ):  test_sce_common-tissue-large-buffer-size
+START TEST ( 2023-11-15 01:54:16.314651 ):  test_sce_common-cell-type
+END TEST ( 2023-11-15 04:09:16.883595 ):  test_sce_common-cell-type
+START TEST ( 2023-11-15 04:09:16.888096 ):  test_sce_common-cell-type-large-buffer-size
+END TEST ( 2023-11-15 06:33:44.356152 ):  test_sce_common-cell-type-large-buffer-size
+START TEST ( 2023-11-15 06:33:44.367543 ):  test_sce_whole-enchilada-large-buffer-size
+END TEST ( 2023-11-15 06:33:44.40467 ):  test_sce_whole-enchilada-large-buffer-size
+```
+
+- `acceptance-tests-logs-2023-11-14.csv`
+
+```text
+test,user,system,real,test_result
+test_load_obs_human,10.872,45.086,6.811,expect_true(nrow(obs_df) > 0): expectation_success: nrow(obs_df) > 0 is not TRUE
+test_load_var_human,0.426,0.638999999999996,2.448,expect_true(nrow(var_df) > 0): expectation_success: nrow(var_df) > 0 is not TRUE
+test_load_obs_mouse,1.673,6.315,2.579,expect_true(nrow(obs_df) > 0): expectation_success: nrow(obs_df) > 0 is not TRUE
+test_load_var_mouse,0.51,1.719,2.524,expect_true(nrow(var_df) > 0): expectation_success: nrow(var_df) > 0 is not TRUE
+test_incremental_read_obs_human,10.135,35.531,4.384,expect_true(table_iter_is_ok(obs_iter)): expectation_success: table_iter_is_ok(obs_iter) is not TRUE
+test_incremental_read_var_human,0.457000000000001,0.575000000000003,2.296,expect_true(table_iter_is_ok(var_iter)): expectation_success: table_iter_is_ok(var_iter) is not TRUE
+test_incremental_read_obs_mouse,1.701,7.48899999999999,2.699,expect_true(table_iter_is_ok(obs_iter)): expectation_success: table_iter_is_ok(obs_iter) is not TRUE
+test_incremental_read_var_mouse,0.701999999999998,1.917,2.427,expect_true(table_iter_is_ok(var_iter)): expectation_success: table_iter_is_ok(var_iter) is not TRUE
+test_incremental_read_X_human,9478.638,20812.554,1540.115,expect_true(table_iter_is_ok(X_iter)): expectation_success: table_iter_is_ok(X_iter) is not TRUE
+test_incremental_read_X_human-large-buffer-size,9515.992,58285.962,2095.726,expect_true(table_iter_is_ok(X_iter)): expectation_success: table_iter_is_ok(X_iter) is not TRUE
+test_incremental_read_X_mouse,1003.918,1709.78600000001,158.077,expect_true(table_iter_is_ok(X_iter)): expectation_success: table_iter_is_ok(X_iter) is not TRUE
+test_incremental_read_X_mouse-large-buffer-size,1003.373,1704.70299999999,141.275,expect_true(table_iter_is_ok(X_iter)): expectation_success: table_iter_is_ok(X_iter) is not TRUE
+test_incremental_query_human_brain,222.373,217.176999999996,97.7020000000002,expect_true(table_iter_is_ok(query$obs())): expectation_success: table_iter_is_ok(query$obs()) is not TRUE ; expect_true(table_iter_is_ok(query$var())): expectation_success: table_iter_is_ok(query$var()) is not TRUE ; expect_true(table_iter_is_ok(query$X("raw")$tables())): expectation_success: table_iter_is_ok(query$X("raw")$tables()) is not TRUE
+test_incremental_query_human_aorta,11.768,25.8989999999903,13.5769999999998,expect_true(table_iter_is_ok(query$obs())): expectation_success: table_iter_is_ok(query$obs()) is not TRUE ; expect_true(table_iter_is_ok(query$var())): expectation_success: table_iter_is_ok(query$var()) is not TRUE ; expect_true(table_iter_is_ok(query$X("raw")$tables())): expectation_success: table_iter_is_ok(query$X("raw")$tables()) is not TRUE
+test_incremental_query_mouse_brain,69.893,62.8429999999935,28.4569999999999,expect_true(table_iter_is_ok(query$obs())): expectation_success: table_iter_is_ok(query$obs()) is not TRUE ; expect_true(table_iter_is_ok(query$var())): expectation_success: table_iter_is_ok(query$var()) is not TRUE ; expect_true(table_iter_is_ok(query$X("raw")$tables())): expectation_success: table_iter_is_ok(query$X("raw")$tables()) is not TRUE
+test_incremental_query_mouse_aorta,21.607,15.0279999999912,10.5630000000001,expect_true(table_iter_is_ok(query$obs())): expectation_success: table_iter_is_ok(query$obs()) is not TRUE ; expect_true(table_iter_is_ok(query$var())): expectation_success: table_iter_is_ok(query$var()) is not TRUE ; expect_true(table_iter_is_ok(query$X("raw")$tables())): expectation_success: table_iter_is_ok(query$X("raw")$tables()) is not TRUE
+test_seurat_small-query,22.7649999999994,24.9000000000087,26.1269999999995,test_seurat(test_args): expectation_success: is(this_seurat, "Seurat") is not TRUE ; test_seurat(test_args): expectation_success: ncol(this_seurat) > 0 is not TRUE
+test_seurat_10K-cells-human,18.5760000000009,14.9829999999929,24.0699999999997,test_seurat(test_args): expectation_success: is(this_seurat, "Seurat") is not TRUE ; test_seurat(test_args): expectation_success: ncol(this_seurat) > 0 is not TRUE
+test_seurat_100K-cells-human,100.204999999998,81.8509999999951,102.676,test_seurat(test_args): expectation_success: is(this_seurat, "Seurat") is not TRUE ; test_seurat(test_args): expectation_success: ncol(this_seurat) > 0 is not TRUE
+test_seurat_250K-cells-human,260.911,210.860000000001,260.016000000001,test_seurat(test_args): expectation_success: is(this_seurat, "Seurat") is not TRUE ; test_seurat(test_args): expectation_success: ncol(this_seurat) > 0 is not TRUE
+test_seurat_500K-cells-human,188.810000000001,300.986000000004,94.7910000000002,test_seurat(test_args): Error: Error in `vec_to_Array(x, type)`: long vectors not supported yet: memory.c:3888
+test_seurat_750K-cells-human,286.100999999999,476.747999999992,131.579,test_seurat(test_args): Error: Error in `vec_to_Array(x, type)`: long vectors not supported yet: memory.c:3888
+test_seurat_1M-cells-human,395.574000000001,664.184999999998,175.497,test_seurat(test_args): Error: Error in `vec_to_Array(x, type)`: long vectors not supported yet: memory.c:3888
+test_seurat_common-tissue,479.936999999998,267.138000000006,395.615000000001,test_seurat(test_args): expectation_success: is(this_seurat, "Seurat") is not TRUE ; test_seurat(test_args): expectation_success: ncol(this_seurat) > 0 is not TRUE
+test_seurat_common-tissue-large-buffer-size,475.071,256.587,385.736,test_seurat(test_args): expectation_success: is(this_seurat, "Seurat") is not TRUE ; test_seurat(test_args): expectation_success: ncol(this_seurat) > 0 is not TRUE
+test_seurat_common-cell-type,3491.464,25131.535,2197.378,test_seurat(test_args): Error: Error in `vec_to_Array(x, type)`: long vectors not supported yet: memory.c:3888
+test_seurat_common-cell-type-large-buffer-size,3723.685,20053.295,3740.85,test_seurat(test_args): Error: Error in `vec_to_Array(x, type)`: long vectors not supported yet: memory.c:3888
+test_seurat_whole-enchilada-large-buffer-size,0.00599999999758438,0,0.0069999999996071,expect_true(TRUE): expectation_success: TRUE is not TRUE
+test_sce_small-query,20.3489999999983,32.1600000000035,25.8549999999996,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_10K-cells-human,14.1660000000011,15.6140000000014,19.509,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_100K-cells-human,73.7009999999973,71.1290000000008,64.0109999999986,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_250K-cells-human,169.846000000001,211.741999999998,144.201000000001,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_500K-cells-human,327.071,422.800999999992,345.055999999999,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_750K-cells-human,510.853000000003,556.207999999984,420.038,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_1M-cells-human,681.242000000002,793.367999999988,555.210999999999,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_common-tissue,302.630999999998,240.524999999994,172.307999999999,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_common-tissue-large-buffer-size,298.978000000003,251.656999999977,167.74,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_common-cell-type,6661.194,14059.888,8100.566,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_common-cell-type-large-buffer-size,6539.15100000001,12091.293,8667.461,test_sce(test_args): expectation_success: is(this_sce, "SingleCellExperiment") is not TRUE ; test_sce(test_args): expectation_success: ncol(this_sce) > 0 is not TRUE
+test_sce_whole-enchilada-large-buffer-size,0.010999999998603,0.00699999998323619,0.0349999999998545,expect_true(TRUE): expectation_success: TRUE is not TRUE
+```
+
 ### 2023-07-15
 
 - Host: EC2 instance type: `r6id.x32xlarge`, all nvme mounted as swap.
diff --git a/docs/conf.py b/docs/conf.py
index 58b460780..b8de639b3 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -7,8 +7,8 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
 project = 'cellxgene-census'
-copyright = '2022-2023 Chan Zuckerberg Initiative'
-author = 'Chan Zuckerberg Initiative'
+copyright = '2022-2023 Chan Zuckerberg Initiative Foundation'
+author = 'Chan Zuckerberg Initiative Foundation'
 
 import git
 repo = git.Repo(search_parent_directories=True)
diff --git a/tools/cellxgene_census_builder/pyproject.toml b/tools/cellxgene_census_builder/pyproject.toml
index f90b6c459..952c6ccf0 100644
--- a/tools/cellxgene_census_builder/pyproject.toml
+++ b/tools/cellxgene_census_builder/pyproject.toml
@@ -7,7 +7,7 @@ name = "cellxgene_census_builder"
 dynamic = ["version"]
 description = "Build Cell Census"
 authors = [
-    { name = "Chan Zuckerberg Initiative", email = "soma@chanzuckerberg.com" }
+    { name = "Chan Zuckerberg Initiative Foundation", email = "soma@chanzuckerberg.com" }
 ]
 license = { text = "MIT" }
 readme = "README.md"