popv.algorithms.KNN_BBKNN

popv.algorithms.KNN_BBKNN#

class popv.algorithms.KNN_BBKNN(batch_key='_batch_annotation', labels_key='_labels_annotation', result_key='popv_knn_bbknn_prediction', umap_key='X_umap_bbknn_popv', method_kwargs=None, classifier_kwargs=None, embedding_kwargs=None)[source]#

Class to compute KNN classifier after BBKNN integration.

Parameters:

  • batch_key (str | None (default: '_batch_annotation')) – Key in obs field of adata for batch information. Default is “_batch_annotation”.

  • labels_key (str | None (default: '_labels_annotation')) – Key in obs field of adata for cell-type information. Default is “_labels_annotation”.

  • result_key (str | None (default: 'popv_knn_bbknn_prediction')) – Key in obs in which celltype annotation results are stored. Default is “popv_knn_bbknn_prediction”.

  • umap_key (str | None (default: 'X_umap_bbknn_popv')) – Key in obsm in which UMAP embedding of integrated data is stored. Default is “X_umap_bbknn_popv”.

  • method_kwargs (dict | None (default: None)) – Additional parameters for BBKNN. See scanpy.external.pp.bbknn(). Default is {“metric”: “euclidean”, “approx”: True, “n_pcs”: 50, “neighbors_within_batch”: 3, “use_annoy”: False}.

  • classifier_kwargs (dict | None (default: None)) – Dictionary to supply non-default values for KNN classifier. See sklearn.neighbors.KNeighborsClassifier. Default is {“weights”: “uniform”, “n_neighbors”: 15}.

  • embedding_kwargs (dict | None (default: None)) – Dictionary to supply non-default values for UMAP embedding. See scanpy.tl.umap(). Default is {“min_dist”: 0.1}.

Methods table#

compute_integration(adata)

Compute BBKNN integration.

compute_umap(adata)

Compute UMAP embedding of integrated data.

predict(adata)

Predict celltypes using BBKNN kNN.

Methods#

KNN_BBKNN.compute_integration(adata)[source]#

Compute BBKNN integration.

Parameters:

adata – AnnData object. Modified inplace.

KNN_BBKNN.compute_umap(adata)[source]#

Compute UMAP embedding of integrated data.

Parameters:

adata – AnnData object. Results are stored in adata.obsm[self.umap_key].

KNN_BBKNN.predict(adata)[source]#

Predict celltypes using BBKNN kNN.

Parameters:

adata – Anndata object. Results are stored in adata.obs[self.result_key].

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