gatac.tl.chromvar

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gatac.tl.chromvar#

gatac.tl.chromvar(adata, genome_fasta, motifs_path, *, method='chromvar', n_iterations=50, pvalue=5e-05, check_rc=True, bg='subject', coordinate_system='0-based', batch_size=5000, motif_batch_size=-1, key_added='chromvar', return_adata=False)#

Run the full chromVAR pipeline in a single call.

Executes the following steps in order:

  1. compute_peak_bias — GC content from genome FASTA

  2. sample_bg_peaks — background peak sampling

  3. read_motifs + scan_motifs — motif matching

  4. compute_deviations — TF deviation scores

Parameters:
adata AnnData

Peak-level AnnData (cells × peaks).

genome_fasta str or Path

Path to genome FASTA file.

motifs_path str or Path

Path to motif file in MEME format.

method {"knn", "chromvar"}, default "chromvar"

Background sampling method passed to sample_bg_peaks.

n_iterations int, default 50

Number of background peaks to sample per peak.

pvalue float, default 5e-5

P-value threshold for motif matching.

check_rc bool, default True

Whether to scan both strands.

bg str or tuple, default "subject"

Background nucleotide probabilities for motif scoring.

coordinate_system {"0-based", "1-based"}, default "0-based"

Coordinate system of peak names in adata.var_names.

batch_size int, default 5000

Number of cells per GPU batch in compute_deviations.

motif_batch_size int, default -1

Number of motifs per chunk in compute_deviations.

key_added str, default "chromvar"

Key under which the deviation DataFrame is stored in adata.obsm.

return_adata bool, default False

If True, also return a new AnnData with deviations as .X.

Returns:

None or AnnData Always stores deviations as a DataFrame in adata.obsm[key_added]. Returns an AnnData (cells × motifs) only when return_adata=True.

Return type:

AnnData | None

Examples

>>> import gatac as ga
>>> ga.tl.chromvar(
...     peak_adata,
...     "../resources/GRCh38.p13.genome.fa",
...     "../resources/cisBP_human.meme",
... )
>>> peak_adata.obsm["chromvar"]  # DataFrame (cells × motifs)
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