knn_clustering

spac.transformations.knn_clustering(adata, features, annotation, layer=None, k=50, output_annotation='knn', associated_table=None, missing_label='no_label', **kwargs)[source]

Calculate knn clusters using sklearn KNeighborsClassifier

The function will add these two attributes to adata: .obs[output_annotation]

The assigned int64 class labels by KNeighborsClassifier

.uns[output_annotation_features]

The features used to calculate the knn clusters

Parameters:

  • adata (anndata.AnnData) – The AnnData object.

  • features (list of str) – The variables that would be included in fitting the KNN classifier.

  • annotation (str) – The name of the annotation used for classifying the data

  • layer (str, optional) – The layer to be used.

  • k (int, optional) – The number of nearest neighbor to be used in creating the graph.

  • output_annotation (str, optional) – The name of the output layer where the clusters are stored.

  • associated_table (str, optional) – If set, use the corresponding key adata.obsm to calcuate the clustering. Takes priority over the layer argument.

  • missing_label (str or int) – The value of missing annotations in adata.obs[annotation]

Returns:

adata is updated inplace

Return type:

None