decoupler.run_wsum
- decoupler.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:
- matlist, DataFrame or AnnData
List of [features, matrix], dataframe (samples x features) or an AnnData instance.
- netDataFrame
Network in long format.
- sourcestr
Column name in net with source nodes.
- targetstr
Column name in net with target nodes.
- weightstr
Column name in net with weights.
- timesint
How many random permutations to do.
- batch_sizeint
Size of the batches to use. Increasing this will consume more memmory but it will run faster.
- min_nint
Minimum of targets per source. If less, sources are removed.
- seedint
Random seed to use.
- verbosebool
Whether to show progress.
- use_rawbool
Use raw attribute of mat if present.
- Returns:
- estimateDataFrame
WSUM scores. Stored in .obsm[‘wsum_estimate’] if mat is AnnData.
- normDataFrame
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.