pegasus.louvain
- pegasus.louvain(data, rep='pca', resolution=1.3, n_clust=None, random_state=0, class_label='louvain_labels')[source]
Cluster the cells using Louvain algorithm. [Blondel08]
- 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 indata.obsm
and nearest neighbors must be calculated so that affinity matrix'W_' + rep
exists indata.uns
. By default, use PCA coordinates.resolution (
int
, optional, default:1.3
) – Resolution factor. Higher resolution tends to find more clusters with smaller sizes.n_clust (
int
, optional, default:None
) – This option only takes effect if ‘resolution = None’. Try to find an appropriate resolution by binary search such that the total number of clusters matches ‘n_clust’. The range of resolution to search is (0.01, 2.0].random_state (
int
, optional, default:0
) – Random seed for reproducing results.class_label (
str
, optional, default:"louvain_labels"
) – Key name for storing cluster labels indata.obs
.
- Return type
None
- Returns
None
Update
data.obs
–data.obs[class_label]
: Cluster labels of cells as categorical data.
Examples
>>> pg.louvain(data)