stlearn.em.run_diffmap¶
- stlearn.em.run_diffmap(adata: AnnData, n_comps: int = 15, copy: bool = False)[source]¶
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
neighbors()
. To reproduce the original implementation using a Gaussian kernel, use method==’gauss’ inneighbors()
. To use an exponential kernel, use the default method==’umap’. Differences between these options shouldn’t usually be dramatic. :param adata: Annotated data matrix. :param n_comps: The number of dimensions of the representation. :param copy: Return a copy instead of writing to adata.- Returns:
Depending on copy, returns or updates adata with the following fields.
`X_diffmap` (
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` (
numpy.ndarray
(adata.uns)) – Array of size (number of eigen vectors). Eigenvalues of transition matrix.