rapids_singlecell.dcg.run_wsum

rapids_singlecell.dcg.run_wsum(mat, net, *, source='source', target='target', weight='weight', times=1000, batch_size=10000, min_n=5, seed=42, verbose=False, use_raw=True)

Weighted sum (WSUM). WSUM infers regulator activities by first multiplying each target feature by its associated weight which then are summed to an enrichment score (wsum_estimate). Furthermore, permutations of random target features can be performed to obtain a null distribution that can be used to compute a z-score (wsum_norm), or a corrected estimate (wsum_corr) by multiplying wsum_estimate by the minus log10 of the obtained empirical p-value.

Parameters:

mat AnnData | DataFrame | list

List of [features, matrix], dataframe (samples x features) or an AnnData instance.

net DataFrame

Network in long format.

source default: 'source'

Column name in net with source nodes.

target default: 'target'

Column name in net with target nodes.

weight default: 'weight'

Column name in net with weights.

times default: 1000

How many random permutations to do.

batch_size int (default: 10000)

Size of the batches to use. Increasing this will consume more memory but it will run faster.

min_n int (default: 5)

Minimum of targets per source. If less, sources are removed.

seed int (default: 42)

Random seed to use.

verbose bool (default: False)

Whether to show progress.

use_raw bool (default: True)

Use raw attribute of mat if present.

Return type:

tuple | None

Returns:

Updates adata with the following fields.

estimateDataFrame

WSUM scores. Stored in .obsm['wsum_estimate'] if mat is AnnData.

norm: DataFrame

Normalized WSUM scores. Stored in .obsm['wsum_norm'] if mat is AnnData.

corrDataFrame

Corrected WSUM scores. Stored in .obsm['wsum_corr'] if mat is AnnData.

pvalsDataFrame

Obtained p-values. Stored in .obsm['wsum_pvals'] if mat is AnnData.