snapatac2.tl.kmeans#
- snapatac2.tl.kmeans(adata, n_clusters, n_iterations=-1, random_state=0, use_rep='X_spectral', key_added='kmeans', inplace=True)[source]#
Cluster cells into subgroups using the K-means algorithm, a classical algorithm in data mining.
- Parameters:
adata (
AnnData|AnnDataSet|ndarray) – The annotated data matrix.n_clusters (
int) – Number of clusters to return.n_iterations (
int) – How many iterations of the kmeans clustering algorithm to perform. Positive values above 2 define the total number of iterations to perform, -1 has the algorithm run until it reaches its optimal clustering.random_state (
int) – Change the initialization of the optimization.use_rep (
str) – Which data inadata.obsmto use for clustering. Default is “X_spectral”.key_added (
str) –adata.obskey under which to add the cluster labels.
- Return type:
- Returns:
adds fields to
adataadata.obs[key_added]– Array of dim (number of samples) that stores the subgroup id ('0','1', …) for each cell.adata.uns['kmeans']['params']– A dict with the values for the parametersn_clusters,random_state, andn_iterations.