snapatac2.tl.add_cor_scores#

snapatac2.tl.add_cor_scores(network, *, gene_mat=None, peak_mat=None, select=None, overwrite=False)[source]#

Add Spearman correlation scores to network edges.

Use this function to score existing region-gene, gene-gene, or motif-region associations from matched cell-by-peak and cell-by-gene matrices.

Anti-Patterns#

  • Do NOT pass matrices with different cell order or different obs_names.

  • Do NOT expect missing edges to be created; only existing network edges are scored.

param network:

Graph containing NodeData nodes and LinkData edges.

type network:

PyDiGraph

param gene_mat:

Cell-by-gene matrix with gene names in .var_names.

type gene_mat:

AnnData | AnnDataSet | None

param peak_mat:

Cell-by-region matrix with region names in .var_names.

type peak_mat:

AnnData | AnnDataSet | None

param select:

Gene ids to score. If None, score all eligible target genes.

type select:

list[str] | None

param overwrite:

If True, recompute existing cor_score values.

type overwrite:

bool

returns:

Updates edge cor_score attributes in network in place.

rtype:

None

Examples

>>> import snapatac2 as snap
>>> adata = snap.datasets.pbmc5k(type="annotated_h5ad")
>>> gene_mat = snap.pp.make_gene_matrix(adata, snap.genome.hg38)
>>> network = snap.tl.init_network_from_annotation(list(adata.var_names[:10]), snap.genome.hg38)
>>> snap.tl.add_cor_scores(network, peak_mat=adata, gene_mat=gene_mat)
>>> network.num_edges() >= 0
True
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