diff --git a/notebooks/preprocess.ipynb b/notebooks/preprocess.ipynb new file mode 100644 index 000000000..25ead9f45 --- /dev/null +++ b/notebooks/preprocess.ipynb @@ -0,0 +1,182 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!aws s3 cp s3://openproblems-bio/public/neurips-2023-competition/sc_counts.h5ad ./resources_raw/ --no-sign-request\n", + "!aws s3 cp s3://openproblems-bio/public/neurips-2023-competition/multiome_counts.h5mu ./resources_raw/ --no-sign-request" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install sctk, anndata\n", + "\n", + "import anndata as ad \n", + "import pandas as pd\n", + "import numpy as np\n", + "import sctk\n", + "par = {\n", + " 'sc_counts': '../resources_raw/sc_counts.h5ad',\n", + " 'sc_counts_filtered': '../resources_raw/sc_counts_filtered.h5ad',\n", + "}\n", + "def preprocess_sc(par):\n", + " # clean up\n", + " sc_counts = ad.read_h5ad(par['sc_counts'])\n", + " sc_counts.obs = sc_counts.obs[['well', 'row', 'col', 'plate_name', 'cell_type', 'donor_id']]\n", + " sc_counts.X = sc_counts.layers['counts']\n", + " del sc_counts.layers \n", + " del sc_counts.obsm \n", + " sc_counts.var_names_make_unique()\n", + " sc_counts.obs['plate_well_cell_type'] = sc_counts.obs['plate_name'].astype('str') \\\n", + " + '_' + sc_counts.obs['well'].astype('str') \\\n", + " + '_' + sc_counts.obs['cell_type'].astype('str')\n", + " sc_counts.obs['plate_well_cell_type'] = sc_counts.obs['plate_well_cell_type'].astype('category')\n", + "\n", + " # merge cell types\n", + " CELL_TYPES = ['NK cells', 'T cells CD4+', 'T cells CD8+', 'T regulatory cells', 'B cells', 'Myeloid cells']\n", + " T_cell_types = ['T regulatory cells', 'T cells CD8+', 'T cells CD4+']\n", + " cell_type_map = {cell_type: 'T cells' if cell_type in T_cell_types else cell_type for cell_type in CELL_TYPES}\n", + " sc_counts.obs['cell_type'] = sc_counts.obs['cell_type'].map(cell_type_map)\n", + " sc_counts.obs['cell_type'].unique()\n", + "\n", + " # qc \n", + " sctk.calculate_qc(sc_counts)\n", + " sctk.cellwise_qc(sc_counts)\n", + "\n", + " # filtering\n", + " # cell wise\n", + " filter_percent_hb = sc_counts.obs.percent_hb>.2\n", + " filter_percent_hb.sum()\n", + " # gene wise\n", + " plates = sc_counts.obs['plate_name'].unique()\n", + "\n", + " # Step 2: Initialize a DataFrame to store counts\n", + " gene_counts_per_plate = pd.DataFrame(index=sc_counts.var_names, columns=plates, dtype=int)\n", + "\n", + " # Step 3: Iterate over each plate and calculate expression counts\n", + " for plate in plates:\n", + " # Subset the AnnData object for the current plate\n", + " subset = sc_counts[sc_counts.obs['plate_name'] == plate]\n", + "\n", + " # Calculate expression counts (genes x cells > 0)\n", + " expressed_genes = (subset.X > 0).sum(axis=0)\n", + "\n", + " # Check if the result needs conversion from sparse matrix format\n", + " if isinstance(expressed_genes, np.matrix):\n", + " expressed_genes = np.array(expressed_genes).flatten()\n", + "\n", + " # Store the counts in the DataFrame\n", + " gene_counts_per_plate[plate] = expressed_genes\n", + "\n", + " # Step 4: Aggregate counts across plates (max or sum based on the requirement)\n", + " # We use `max` here to find if any gene meets the criteria in at least one plate\n", + " max_counts = gene_counts_per_plate.max(axis=1)\n", + "\n", + " # Step 5: Create a mask for genes to keep (genes expressed in at least 100 cells in any plate)\n", + " genes_to_keep = max_counts >= 100\n", + " print('retained genes:', genes_to_keep.sum())\n", + " # actual filtering\n", + " sc_counts = sc_counts[(~filter_percent_hb), genes_to_keep]\n", + " # clean\n", + " sc_counts.obs = sc_counts.obs[['cell_type', 'sm_name', 'donor_id', 'row', 'plate_name', 'well']]\n", + " sc_counts.var = sc_counts.var[[]]\n", + "\n", + " del sc_counts.obsm\n", + " del sc_counts.uns\n", + "\n", + " sc_counts.write(par['sc_counts_filtered'])\n", + "preprocess_sc(par)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import anndata as ad \n", + "from scipy import sparse\n", + "par = {\n", + " 'sc_counts_filtered': '../resources_raw/sc_counts_filtered.h5ad',\n", + " 'pseudobulked': '../resources_raw/pseudobulked.h5ad',\n", + "}\n", + "def sum_by(adata: ad.AnnData, col: str) -> ad.AnnData:\n", + " \"\"\"\n", + " Adapted from this forum post: \n", + " https://discourse.scverse.org/t/group-sum-rows-based-on-jobs-feature/371/4\n", + " \"\"\"\n", + " \n", + " assert pd.api.types.is_categorical_dtype(adata.obs[col])\n", + "\n", + " # sum `.X` entries for each unique value in `col`\n", + " cat = adata.obs[col].values\n", + "\n", + " indicator = sparse.coo_matrix(\n", + " (\n", + " np.broadcast_to(True, adata.n_obs),\n", + " (cat.codes, np.arange(adata.n_obs))\n", + " ),\n", + " shape=(len(cat.categories), adata.n_obs),\n", + " )\n", + " \n", + " sum_adata = ad.AnnData(\n", + " indicator @ adata.X,\n", + " var=adata.var,\n", + " obs=pd.DataFrame(index=cat.categories),\n", + " )\n", + " \n", + " # copy over `.obs` values that have a one-to-one-mapping with `.obs[col]`\n", + " obs_cols = adata.obs.columns\n", + " obs_cols = list(set(adata.obs.columns) - set([col]))\n", + " \n", + " one_to_one_mapped_obs_cols = []\n", + " nunique_in_col = adata.obs[col].nunique()\n", + " for other_col in obs_cols:\n", + " if len(adata.obs[[col, other_col]].drop_duplicates()) == nunique_in_col:\n", + " one_to_one_mapped_obs_cols.append(other_col)\n", + "\n", + " joining_df = adata.obs[[col] + one_to_one_mapped_obs_cols].drop_duplicates().set_index(col)\n", + " assert (sum_adata.obs.index == sum_adata.obs.join(joining_df).index).all()\n", + " sum_adata.obs = sum_adata.obs.join(joining_df)\n", + " sum_adata.obs.index.name = col\n", + " sum_adata.obs = sum_adata.obs.reset_index()\n", + " sum_adata.obs.index = sum_adata.obs.index.astype('str')\n", + "\n", + " return sum_adata\n", + "def pseudobulked_and_filter(par):\n", + " # pseudobulk\n", + " sc_counts = ad.read_h5ad(par['sc_counts_filtered'])\n", + " bulk_adata = sum_by(sc_counts, 'plate_well_cell_type')\n", + " bulk_adata.obs['cell_count'] = sc_counts.obs.groupby('plate_well_cell_type').size().values\n", + " bulk_adata.X = np.array(bulk_adata.X.todense())\n", + "\n", + " print('ratio of missingness' , (bulk_adata.X==0).sum()/bulk_adata.X.size)\n", + " bulk_adata.var = bulk_adata.var.reset_index()\n", + " bulk_adata.var.set_index('index', inplace=True)\n", + "\n", + " bulk_adata.X = np.nan_to_num(bulk_adata.X, nan=0)\n", + "\n", + " \n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git 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b/src/workflows/run_benchmark/config.vsh.yaml index ba46c1230..da17eb566 100644 --- a/src/workflows/run_benchmark/config.vsh.yaml +++ b/src/workflows/run_benchmark/config.vsh.yaml @@ -51,14 +51,9 @@ functionality: entrypoint: run_wf - type: file path: "../../api/task_info.yaml" - dependencies: - - name: common/extract_metadata - repository: openproblemsv2 # dependencies: - # # - name: common/extract_metadata - # # repository: openproblemsv2 - # # - name: control_methods/positive_control - # # - name: control_methods/negative_control + # - name: common/extract_metadata + # repository: openproblemsv2 # - name: methods/scglue # - name: metrics/regression_1 # repositories: