0.12.1

Updates:

• Add support for running arboreto_with_multiprocessing.py with spawn instead of fork as multiprocessing method.Pool
• Use ravel instead of flatten to avoid unnecessary memory copy in aucell
• Update Docker image file and add separated Docker file for pySCENIC with scanpy.

0.12.0

Updates:

• Only databases in Feather v2 format are supported now (ctxcore >= 0.2), which allow uses recent versions of pyarrow (>=8.0.0) instead of very old ones (<0.17). Databases in the new format can be downloaded from https://resources.aertslab.org/cistarget/databases/ and end with *.genes_vs_motifs.rankings.feather or *.genes_vs_tracks.rankings.feather.
• Support clustered motif databases.
• Use custom multiprocessing instead of dask, by default.
• Docker image uses python 3.10 and contains only needed pySCENIC dependencies for CLI usage.
• Remove unneeded scripts and notebooks for unused/deprecated database formats.

0.11.2

Major changes:

• Split some core cisTarget functions out into a separate repository, ctxcore. This is now a required package for pySCENIC.
• Documentation updates

0.11.1

• Fix bug in motif url construction (#275)
• Fix for export2loom with sparse dataframe (#278)
• Fix sklearn t-SNE import (#285)
• Updates to Docker image (expose port 8787 for Dask dashboard)

0.11.0

Major features:

• Updated Arboreto release (GRN inference step) includes:

  • Support for sparse matrices (using the --sparse flag in pyscenic grn, or passing a sparse matrix to grnboost2/genie3).
  • Fixes to avoid dask metadata mismatch error

• Updated cisTarget:

  • Fix for metadata mismatch in ctx prune2df step
  • Support for databases Apache Parquet format
  • Faster loading from feather databases
  • Bugfix: loading genes from a database (previously missing the last gene name in the database)

• Support for Anndata input and output

• Package updates:

  • Upgrade to newer pandas version
  • Upgrade to newer numba version
  • Upgrade to newer versions of dask, distributed

• Input checks and more descriptive error messages.

  • Check that regulons loaded are not empty.

• Bugfixes:

  • In the regulons output from the cisTarget step, the gene weights were incorrectly assigned to their respective target genes (PR #254).
  • Motif url construction fixed when running ctx without pruning
  • Compression of intermediate files in the CLI steps
  • Handle loom files with non-standard gene/cell attribute names
  • Reformat the genesig gmt input/output
  • Fix AUCell output to loom with non-standard loom attributes

0.10.4

Updates:

• Included new (optional) CLI option to add correlation information to the GRN adjacencies file. This can be called with pyscenic add_cor. (vib-singlecell-nf/vsn-pipelines/issues/254)
  • The correlation calculation is subsequently skipped if using this adjacencies + correlations file as the input into pyscenic ctx.

0.10.3

Updates:

• Fix bug in motif url construction (#158)
• Integrate arboreto multiprocessing script into pySCENIC CLI
• cisTarget step: Check for modules with zero db overlap and skip them (#158, #177, #132, #85)
• Bugfix in TF-gene correlation calculation. Quit with error if there is a mismatch between the genes present in the GRN and the expression matrix (#103, #149)
• Error message when regulons file is empty (#133)

0.10.2

Updates:

• Bugfix for CLI grn step

0.10.1

Updates:

• CLI: file compression (optionally) enabled for intermediate files for the major steps: grn (adjacencies matrix), ctx (regulons), and aucell (auc matrix). Compression is used when the file name argument has a .gz ending.
• Restrict packages (pyarrow, pandas) for compatibility.

0.10.0

Updates:

• Added a helper script scripts/arboreto_with_multiprocessing.py that runs the Arboreto GRN algorithms (GRNBoost2, GENIE3) without Dask for compatibility.
• Initial support for the use of sparse expression matrices (applies only to the GRN step in the CLI currently). Using sparse matrices with the GRN step requires a patch to Arboreto (tmoerman/arboreto#20)
• AUCell uses a random sampling of the expression matrix to break ties in the ranking step. The CLI parameter --seed or aucell function parameter seed uses a fixed seed for this step. The regulon thresholds also depend on random sampling in the binarization step (bimodality test: np.random.uniform), and the seed parameters apply here as well.
• Fixed typo in regulon threshold labeling
• Security patch: Bump bleach from 3.1.0 to 3.1.1.