scib.metrics.isolated_labels_asw

scib.metrics.isolated_labels_asw(adata, label_key, batch_key, embed, iso_threshold=None, scale=True, verbose=True)

Isolated label score ASW

Score how well isolated labels are distinguished from all other labels using the average-width silhouette score (ASW) silhouette().

Parameters:

  • adata – anndata object

  • label_key – column in adata.obs

  • batch_key – column in adata.obs

  • embed – key in adata.obsm used for func:~scib.metrics.silhouette

  • iso_threshold – max number of batches per label for label to be considered as isolated, if iso_threshold is integer. If iso_threshold=None, consider minimum number of batches that labels are present in

  • scale – Whether to scale the score between 0 and 1. Only relevant for ASW scores.

  • verbose –

Params **kwargs:

additional arguments to be passed to cluster_optimal_resolution()

Returns:

Mean of ASW over all isolated labels

The function requires an embedding to be stored in adata.obsm and can only be applied to feature and embedding integration outputs. Please note, that the metric cannot be used to evaluate kNN graph outputs. See User Guide for more information on preprocessing.

Examples

# full feature output
scib.pp.reduce_data(
    adata, n_top_genes=2000, batch_key="batch", pca=True, neighbors=False
)
scib.me.isolated_labels_asw(adata, label_key="celltype", embed="X_pca")

# embedding output
scib.me.isolated_labels_asw(
    adata, batch_key="batch", label_key="celltype", embed="X_emb"
)