episcanpy.tl.lazy¶
-
episcanpy.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.
- 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.