episcanpy.api.tl.lazy

episcanpy.api.tl.lazy(adata, pp_pca=True, copy=False)

Automatically computes PCA coordinates, loadings and variance decomposition, a neighborhood graph of observations, t-distributed stochastic neighborhood embedding (tSNE) Uniform Manifold Approximation and Projection (UMAP)

Parameters
adata : AnnData

Annotated data matrix.

pp_pca : bool (default: True)

Computes PCA coordinates before the neighborhood graph

copy : bool (default: False)

Return a copy instead of writing to adata.

Returns

Depending on copy, returns or updates adata with the following fields. X_pca : adata.obsm

PCA coordinates of data.

connectivitiessparse matrix (.uns[‘neighbors’], dtype float32)

Weighted adjacency matrix of the neighborhood graph of data points. Weights should be interpreted as connectivities.

distancessparse matrix (.uns[‘neighbors’], dtype float32)

Instead of decaying weights, this stores distances for each pair of neighbors.

X_tsnenp.ndarray (adata.obs, dtype float)

tSNE coordinates of data.

X_umapadata.obsm

UMAP coordinates of data.