ehrapy.tools.diffmap

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ehrapy.tools.diffmap#

ehrapy.tools.diffmap(edata, *, n_comps=15, neighbors_key=None, random_state=0, copy=False)[source]#

Diffusion Maps [CLL+05], [HBT15], [WHP+19].

Diffusion maps [CLL+05] has been proposed for visualizing biomedical data by [HBT15]. The tool uses the adapted Gaussian kernel suggested by [HBW+16] in the implementation of [WAT18]. The width (“sigma”) of the connectivity kernel is implicitly determined by the number of neighbors used to compute the graph in neighbors(). To reproduce the original implementation using a Gaussian kernel, use method==’gauss’ in neighbors(). To use an exponential kernel, use the default method==’umap’. Differences between these options shouldn’t usually be dramatic.

Parameters:

  • edata (EHRData) – Central data object.

  • n_comps (int, default: 15) – The number of dimensions of the representation. neighbors_key: If not specified, diffmap looks .uns[‘neighbors’] for neighbors settings and .obsp[‘connectivities’], .obsp[‘distances’] for connectivities and distances respectively (default storage places for pp.neighbors). If specified, diffmap looks .uns[neighbors_key] for neighbors settings and .obsp[.uns[neighbors_key][‘connectivities_key’]], .obsp[.uns[neighbors_key][‘distances_key’]] for connectivities and distances respectively.

  • neighbors_key (str | None, default: None) – Key to stored neighbors.

  • random_state (int | RandomState | None, default: 0) – Random seed for the initialization.

  • copy (bool, default: False) – Whether to return a copy of the Data object.

Return type:

EHRData | None

Returns:

Depending on copy, returns or updates edata with the following fields.

X_diffmap : numpy.ndarray (edata.obsm) Diffusion map representation of data, which is the right eigen basis of the transition matrix with eigenvectors as columns.

diffmap_evals : numpy.ndarray (edata.uns) Array of size (number of eigen vectors). Eigenvalues of transition matrix.

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