scib_metrics.silhouette_batch

scib_metrics.silhouette_batch#

scib_metrics.silhouette_batch(X, labels, batch, rescale=True, chunk_size=256, metric='euclidean', between_cluster_distances='nearest')[source]#

Average silhouette width (ASW) with respect to batch ids within each label [Luecken et al., 2022].

Default parameters (‘euclidean’, ‘nearest’) match scIB implementation.

Additional options enable BRAS compatible usage (see bras() documentation).

Parameters:

  • X (ndarray) – Array of shape (n_cells, n_features).

  • labels (ndarray) – Array of shape (n_cells,) representing label values

  • batch (ndarray) – Array of shape (n_cells,) representing batch values

  • rescale (bool (default: True)) – Scale asw into the range [0, 1]. If True, higher values are better.

  • chunk_size (int (default: 256)) – Size of chunks to process at a time for distance computation.

  • metric (Literal['euclidean', 'cosine'] (default: 'euclidean')) – The distance metric to use. The distance function can be ‘euclidean’ (default) or ‘cosine’.

  • between_cluster_distances (Literal['nearest', 'mean_other', 'furthest'] (default: 'nearest')) – Method for computing inter-cluster distances. - ‘nearest’: Standard silhouette (distance to nearest cluster) - ‘mean_other’: BRAS-specific (mean distance to all other clusters) - ‘furthest’: BRAS-specific (distance to furthest cluster)

Return type:

float

Returns:

silhouette score