scib_metrics.utils.silhouette_samples

scib_metrics.utils.silhouette_samples#

scib_metrics.utils.silhouette_samples(X, labels, chunk_size=256, metric='euclidean', between_cluster_distances='nearest')[source]#

Compute the Silhouette Coefficient for each observation.

Implements sklearn.metrics.silhouette_samples().

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) representing a feature array.

  • labels (ndarray) – Array of shape (n_cells,) representing label values for each observation.

  • chunk_size (int (default: 256)) – Number of samples 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:

ndarray

Returns:

silhouette scores array of shape (n_cells,)

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