Breaking changes for 0.4 - simplified IDs, support for external annotations and SCTransform update #20
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…D instead NB: set_var_id_cols! and set_obs_id_cols! are currently removed and will be added again in some form.
Add kwargs `duplicate_var` and `duplicate_obs` to `DataMatrix` and `load10x`.
By default, `feature_type` is included as an additional var ID column if present in features for all samples. Otherwise `extra_var_id_cols` can be passed to `load_counts`/`merge_counts` to specify columns manually. `duplicate_var` and `duplicate_obs` are now properly passed to load/merge/update_matrix functions.
…n data.obs New functionality is not yet tested, but old functionality should work.
…projection thereof. * Annotations now uses get/getindex for basic access * project with NormalizationModel now gives better error message when external annotation is missing * Unit tests
Also move external_obs unit tests to separate @testset.
* Use different IDs in counts_proj to ensure we don't accidently take info from original.
* Support DataFrames where Annotations are supported (for covariates) * _get_df -> get_table * Unit tests
* Remove Annotations from exports. * Update some docstrings and comments.
Refactored to share some code with normalization.
* var_counts_fraction! now supports external_var * bugfix when retrieving external column from Annotations * var_counts_fraction! unit tests with external_var
idf was not properly subsetted when using var_filter in tf_idf_transform, causing an error to be thrown
logtransform and tf_idf_transform now handles duplicate var IDs by optionally including feature_type Corresponding unit tests.
* SCTransformModel now stores var_match (needed for feature_mask) * sctransform sets feature_mask correctly (affects logcellcounts) Unit testing of sctransform: * Variable subsetting using var_filter * Handling of duplicate var IDs
Change regex to simpler startswith.
Add `var_counts_sum`, `var_counts_sum!` and `VarCountsSumModel` that are used for creating annotations by summing over the (chosen) variables in a DataMatrix. In particular, it can be used to compute total RNA counts and total number of RNA variables with nonzero expression.
Better syntax:
`filter_var(a.group=>==("A"), data)`
(where `a::Annotations`.)
Also ensures we don't pass around all annotations when only a single one is needed.
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