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SingleCellProjections.jl changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[Unreleased]

[0.4.1] - 2024-08-27

Added

  • local_outlier_factor, local_outlier_factor_table, local_outlier_factor_projection, local_outlier_factor_projection_table

Fixed

  • Updated tutorial to reflect changes in SingleCellProjections v0.4.

[0.4.0] - 2024-06-20

Breaking

  • DataMatrix will now always use the first column of var/obs annotations as ID. (Multiple ID columns are no longer supported.)
  • load_counts - The default obs ID column name is now "cell_id" (was "id" before).
  • load10x - default to using only first column (id) as unique identifier. Specify e.g. var_id="var_id"=>["id", "feature_type"] to merge multiple columns to create the ID.
  • load10x - default to using first column (barcode) as unique identifier.
  • load10x - no longer supports copy_obs_col kwarg.
  • set_var_id_cols! is replaced with set_var_id_col! (since there is only one ID column).
  • set_obs_id_cols! is replaced with set_obs_id_col! (since there is only one ID column).
  • Update to SCTransform 0.2, which handles logcellcounts better when there are multiple modalities (e.g. RNA and antibody counts) present in the data.

Added

  • var_counts_fraction - Just like var_counts_fraction!, but not modifying the object in place.
  • var_counts_sum and var_counts_sum! - For summing over selected variables. Useful for counting e.g. total RNA expression and finding number of expressed features.
  • Added support for using external annotations where applicable (filter, transforms, normalization, statistical tests, var_counts_fraction!, var_counts_sum!)
  • Added experimental (thus yet unexported) Annotations struct, that wraps a DataFrame with IDs in the first column, and ensures that ID remain when accessing columns. (So that the resulting object can be leftjoined to data.obs/data.var.)

Fixed

  • Add compat for weakdeps (UMAP, TSne, PrincipalMomentAnalysis).
  • SVDModel now only stores U and S since V is not needed for projection.

[0.3.9] - 2024-03-04

Fixed

  • Relax === to == when comparing some models. (This fixes a bug occurring when a model is saved to disk using e.g. JLD2 and the loaded again.)

[0.3.8] - 2024-02-22

Added

  • svd, force_layout and pma now supports seed kwarg. To use it, StableRNGs must be loaded.

[0.3.7] - 2023-12-19

Fixed

  • load_counts - You can now pass a single filename to load a single file (previously arrays were required, but the error message was confusing).

[0.3.6] - 2023-12-15

Added

  • local_outlier_factor! - Compute the Local Outlier Factor (LOF) for each observation in a DataMatrix. Supports finding neighbors in a low dimensional space (e.g. after PCA or UMAP), but computing distances in a high dimensional space (e.g. after normalization).
  • local_outlier_factor_projection! - Compute the Local Outlier Factor (LOF) for each observation in a DataMatrix. Only points in the base data set are considered as neighbors.

Fixed

  • knn_adjacency_matrix - kwarg make_symmetric must now be specified by the caller.

[0.3.5] - 2023-12-12

Added

  • pseudobulk: function used to collapse a DataMatrix into a smaller DataMatrix by averaging over groups of observations.

Fixed

  • Add stdlib compat

[0.3.4] - 2023-09-13

Fixed

  • Add compat with HDF5.jl v0.17

[0.3.3] - 2023-08-16

Fixed

  • UMAP, TSne and PrincipalMomentAnalysis support now uses Package Extensions (on Julia 1.9+)
  • Compat bump for SingleCell10x which should reduce loading time and memory usage when reading from .h5 files

[0.3.2] - 2023-07-17

Fixed

  • Bug fix: Add missing method for SCTransformModel.

[0.3.1] - 2023-07-17

Added

  • Float32 support: sctransform, logtransform and tf_idf_transform now supports an optional type argument T which controls the eltype of the sparse transformed matrix. By setting it to Float32 it is possible to reduce memory usage with little impact on results, since downstream computations are still performed in Float64 precision.

[0.3] - 2023-06-23

Breaking

  • normalize_matrix: Categorical coviariates with missing values will now error.
  • differentialexpression: Removed function. Differential expression is now done with ftest, ttest or mannwhitney instead.
  • logtransform and tf_idf_transform now defaults to only keeping features with feature_type "Gene Expression" (if feature_type is present as a variable annotation).

Added

  • Statistical tests: F-test (ANOVA, Quadratic Regression, etc.), t-tests (Two-Group comparison, linear regression etc.) and MannWhitney U-test (Wilcoxon rank-sum-test).
  • Support for TwoGroup covariates (also useful for normalize_matrix).