epiScanpy
0.3.2+4.g6b24431
Tutorials
Usage Principles
System Requirements
Installation
API
Count Matrices: CT
Preprocessing: PP
Tools: TL
episcanpy.api.tl.rank_features
episcanpy.api.tl.lazy
episcanpy.api.tl.load_markers
episcanpy.api.tl.identify_cluster
episcanpy.api.tl.top_feature_genes
episcanpy.api.tl.var_features_to_genes
episcanpy.api.tl.geneactivity
episcanpy.api.tl.diffmap
episcanpy.api.tl.draw_graph
episcanpy.api.tl.tsne
episcanpy.api.tl.umap
episcanpy.api.tl.dpt
episcanpy.api.tl.louvain
episcanpy.api.tl.leiden
episcanpy.api.tl.kmeans
episcanpy.api.tl.hc
episcanpy.api.tl.getNClusters
episcanpy.api.tl.dendogram
episcanpy.api.tl.ARI
episcanpy.api.tl.AMI
episcanpy.api.tl.homogeneity
episcanpy.api.tl.silhouette
Plotting: PL
References
epiScanpy
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API
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episcanpy.api.tl.kmeans
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episcanpy.api.tl.kmeans
¶
episcanpy.api.tl.
kmeans
(
adata
,
num_clusters
)
¶
Compute kmeans clustering using X_pca fits. random_state = 2019