stereo.core.StPipeline.neighbors

StPipeline.neighbors(pca_res_key='pca', method='umap', metric='euclidean', n_pcs=None, n_neighbors=10, knn=True, n_jobs=10, res_key='neighbors')

Compute a spatial neighborhood graph over all cells.

Parameters:

• pca_res_key (str) – the key of PCA analysis to get corresponding result from self.result.

• method (Literal['umap', 'gauss']) – use umap or gauss to compute connectivities.

• metric (str) –

  a known metric’s name or a callable that returns a distance, include:

  • euclidean

  • manhattan

  • chebyshev

  • minkowski

  • canberra

  • braycurtis

  • mahalanobis

  • wminkowski

  • seuclidean

  • cosine

  • correlation

  • haversine

  • hamming

  • jaccard

  • dice

  • russelrao

  • kulsinski

  • rogerstanimoto

  • sokalmichener

  • sokalsneath

  • yule

• n_pcs (Optional[int]) – the number of principle components to run neighbors, default is None such that self.X is used.

• n_neighbors (int) – the size of nearest neighbors.

• knn (bool) – if True, use a hard threshold to restrict the number of neighbors to n_neighbors, namely consider a knn graph. Otherwise, use a Gaussian Kernel to assign low weights to neighbors more distant than the n_neighbors nearest neighbors.

• n_jobs (int) – the number of parallel running jobs for neighbors, if set to -1, all CPUs will be used. Notice that extremely high value of n_jobs may cause segment fault.

• res_key (str) – the key for storing result of neighbors, default is neighbors.

Returns:

Neighbors result is stored in self.result where the result key is 'neighbors'.