scib_metrics.bras

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scib_metrics.bras#

scib_metrics.bras(X, labels, batch, chunk_size=256, metric='cosine', between_cluster_distances='mean_other')[source]#

Batch removal adapted silhouette (BRAS) for single-cell data integration assessment [Rautenstrauch and Ohler, 2025].

BRAS evaluates batch effect removal with respect to batch ids within each label (cell type cluster), using a modified silhouette score that accounts for nested batch effects. Unlike standard silhouette, BRAS computes between-cluster distances using the between_cluster_distances method rather than nearest-cluster approach. A higher scores indicates better batch mixing.

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

  • between_cluster_distances (Literal['mean_other', 'furthest'] (default: 'mean_other')) – Method for computing inter-cluster distances. - ‘mean_other’: Mean distance to all cells in other clusters (default) - ‘furthest’: Distance to furthest cluster (conservative estimate)

Return type:

float

Returns:

BRAS score

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