episcanpy.tl.silhouette

episcanpy.tl.silhouette(adata_name, cluster_annot, value='X_pca', metric='euclidean', key_added=None, copy=False)

Compute silhouette scores.

It computes the general silhouette score as well as a silhouette score for every cell according to the cell cluster assigned to it.

Parameters

adata_name : AnnData object

cluster_annot : observational variable corresponding to a cell clustering

value : measure used to build the silhouette plot (X_pca, X_tsne, X_umap)

metric : 'euclidean'

key_added : key to save the computed silhouette scores

Returns

general silhouette score in ‘uns’ of the AnnData object individual silhouette scores in ‘obs’ of the AnnData object

Credit to sklearn script : https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py return score and silhouette plot. Still some work to do to finish the function. size=None but you can put ‘large’ if you want a bigger default figure size