scib_metrics.isolated_labels#
- scib_metrics.isolated_labels(X, labels, batch, rescale=True, iso_threshold=None)[source]#
Isolated label score [Luecken et al., 2022].
Score how well labels of isolated labels are distiguished in the dataset by average-width silhouette score (ASW) on isolated label vs all other labels.
The default of the original scib package is to use a cluster-based F1 scoring procedure, but here we use the ASW for speed and simplicity.
- Parameters:
X (
ndarray
) – Array of shape (n_cells, n_features).labels (
ndarray
) – Array of shape (n_cells,) representing label valuesbatch (
ndarray
) – Array of shape (n_cells,) representing batch valuesrescale (
bool
(default:True
)) – Scale asw into the range [0, 1].iso_threshold (
Optional
[int
] (default:None
)) – Max number of batches per label for label to be considered as isolated, if integer. IfNone
, considers minimum number of batches that labels are present in
- Return type:
- Returns:
isolated_label_score