bento.tl.fe

bento.tl.fe(sdata, net, instance_key='cell_boundaries', source='source', target='target', weight='weight', batch_size=10000, min_n=0) → SpatialData

Perform functional enrichment of RNAflux embeddings using decoupler’s wsum function.

Parameters:

  • sdata (SpatialData) – SpatialData object.

  • net (pd.DataFrame) – DataFrame with columns for source, target, and weight. See decoupler API for more details.

  • instance_key (str, default “cell_boundaries”) – Key for the instance in sdata.points.

  • source (str, default “source”) – Column name for source nodes in net.

  • target (str, default “target”) – Column name for target nodes in net.

  • weight (str, default “weight”) – Column name for weights in net.

  • batch_size (int, default 10000) – Number of points to process in each batch.

  • min_n (int, default 0) – Minimum number of targets per source. If less, sources are removed.

Returns:

Updated SpatialData object with: - Enrichment scores added to sdata.points[f”{instance_key}_raster”] - Enrichment statistics added to sdata.tables[“table”].uns[“fe_stats”] and sdata.tables[“table”].uns[“fe_ngenes”]

Return type:

SpatialData