Source code for stlearn.embedding.diffmap

from typing import Optional, Union
import numpy as np
from anndata import AnnData
from numpy.random.mtrand import RandomState
from scipy.sparse import issparse
import scanpy

[docs]def run_diffmap(adata: AnnData, n_comps: int = 15, copy: bool = False): """\ Diffusion Maps [Coifman05]_ [Haghverdi15]_ [Wolf18]_. Diffusion maps [Coifman05]_ has been proposed for visualizing single-cell data by [Haghverdi15]_. The tool uses the adapted Gaussian kernel suggested by [Haghverdi16]_ in the implementation of [Wolf18]_. The width ("sigma") of the connectivity kernel is implicitly determined by the number of neighbors used to compute the single-cell graph in :func:`~scanpy.pp.neighbors`. To reproduce the original implementation using a Gaussian kernel, use `method=='gauss'` in :func:`~scanpy.pp.neighbors`. To use an exponential kernel, use the default `method=='umap'`. Differences between these options shouldn't usually be dramatic. Parameters ---------- adata Annotated data matrix. n_comps The number of dimensions of the representation. copy Return a copy instead of writing to adata. Returns ------- Depending on `copy`, returns or updates `adata` with the following fields. `X_diffmap` : :class:`numpy.ndarray` (`adata.obsm`) Diffusion map representation of data, which is the right eigen basis of the transition matrix with eigenvectors as columns. `diffmap_evals` : :class:`numpy.ndarray` (`adata.uns`) Array of size (number of eigen vectors). Eigenvalues of transition matrix. """, n_comps=n_comps, copy=copy) print( "Diffusion Map is done! Generated in adata.obsm['X_diffmap'] nad adata.uns['diffmap_evals']" ) return adata if copy else None