sccloud.leiden

sccloud.leiden(data, rep='pca', resolution=1.3, n_iter=-1, random_state=0, class_label='leiden_labels')[source]

Cluster the data using Leiden algorithm.

Parameters

  • data (anndata.AnnData) – Annotated data matrix with rows for cells and columns for genes.

  • rep (str, optional, default: "pca") – The embedding representation used for clustering. Keyword 'X_' + rep must exist in data.obsm. By default, use PCA coordinates.

  • resolution (int, optional, default: 1.3) – Resolution factor. Higher resolution tends to find more clusters.

  • n_iter (int, optional, default: -1) – Number of iterations that Leiden algorithm runs. If -1, run the algorithm until reaching its optimal clustering.

  • random_state (int, optional, default: 0) – Random seed for reproducing results.

  • class_label (str, optional, default: "leiden_labels") – Key name for storing cluster labels in data.obs.

Return type

None

Returns

  • None

  • Update data.obs –

    • data.obs[class_label]: Cluster labels of cells as categorical data.

Examples

>>> scc.leiden(adata)