episcanpy.api.pp.lazy¶
- episcanpy.api.pp.lazy(adata, pp_pca=True, svd_solver='arpack', nb_pcs=50, n_neighbors=15, perplexity=30, method='umap', metric='euclidean', min_dist=0.5, spread=1.0, use_highly_variable=False, n_components=2, 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
- svd_solver
- nb_pcs : int (default: 50)
Number of principal component computed for PCA (and therefore neighbors, tsne and umap)
- n_neighbors : int (default: 15)
The size of local neighborhood (in terms of number of neighboring data points) used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100.
- method : str (default: ‘umap’)
Use ‘umap’ or ‘gauss’, kernel for computing connectivities. Gives very similar results.
- metric : str (default: ‘euclidean’)
A known metric’s name or a callable that returns a distance.
- perplexity : int (default: 30)
The perplexity is related to the number of nearest neighbors that is used in other manifold learning algorithms. Larger datasets usually require a larger perplexity. Consider selecting a value between 5 and 50.
- min_dist : float (default: 0.5)
The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points.
- spread : float (default: 1.0)
The effective scale of embedded points. In combination with min_dist this determines how clustered/clumped the embedded points are.
- n_components : int (default: 2)
The number of dimensions of the UMAP embedding.
- copy : bool (default: False)
Return a copy instead of writing to adata.
- method='umap'
- adata :
:param : :param metric=’euclidean’: :param : :param min_dist=0.5: :param : :param spread=1.0: :param : :param n_components=2:
- 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.