snapatac2.pp.knn#

snapatac2.pp.knn(adata, n_neighbors=50, use_dims=None, use_rep='X_spectral', method='hora', n_jobs=-1, inplace=True, random_state=0)[source]#

Compute a neighborhood graph of observations.

Computes a neighborhood graph of observations stored in adata using the method specified by method. The distance metric used is Euclidean.

Parameters:

  • adata (AnnData | AnnDataSet | ndarray) – Annotated data matrix or numpy array.

  • n_neighbors (int) – The number of nearest neighbors to be searched.

  • use_dims (Union[int, list[int], None]) – The dimensions used for computation.

  • use_rep (str) – The key for the matrix

  • method (Literal['hora', 'pynndescent', 'exact']) – ‘hora’, ‘pynndescent’, or ‘exact’. The default is ‘hora’, which uses the HNSW algorithm to approximate the nearest neighbors.

  • n_jobs (int) – number of CPUs to use

  • inplace (bool) – Whether to store the result in the anndata object.

  • random_state (int) – Random seed for approximate nearest neighbor search.

Returns:

if inplace=True, store KNN in .obsp['distances']. Otherwise, return a sparse matrix.

Return type:

csr_matrix | None