pegasus.spectral_leiden

pegasus.spectral_leiden(data, rep='pca', resolution=1.3, rep_kmeans='diffmap', n_clusters=30, n_clusters2=50, n_init=10, n_jobs=- 1, random_state=0, class_label='spectral_leiden_labels')[source]

Cluster the data using Spectral Leiden algorithm. [Li20]

Parameters

  • data (pegasusio.MultimodalData) – 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.

  • rep_kmeans (str, optional, default: "diffmap") – The embedding representation on which the KMeans runs. Keyword must exist in data.obsm. By default, use Diffusion Map coordinates. If diffmap is not calculated, use PCA coordinates instead.

  • n_clusters (int, optional, default: 30) – The number of first level clusters.

  • n_clusters2 (int, optional, default: 50) – The number of second level clusters.

  • n_init (int, optional, default: 10) – Number of kmeans tries for the first level clustering. Default is set to be the same as scikit-learn Kmeans function.

  • n_jobs (int, optional (default: -1)) – Number of threads to use for the KMeans step. -1 refers to using all physical CPU cores.

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

  • class_label (str, optional, default: "spectral_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 for cells as categorical data.

Examples

>>> pg.spectral_leiden(data)