episcanpy.pp.pca

episcanpy.pp.pca(adata, n_comps=50, zero_center=True, svd_solver='auto', random_state=0, return_info=False, use_highly_variable=False, dtype='float32', copy=False, chunked=False, chunk_size=None)

Principal component analysis [Pedregosa11].

Computes PCA coordinates, loadings and variance decomposition. Uses the implementation of scikit-learn [Pedregosa11].

Parameters

data

The (annotated) data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.

n_comps

Number of principal components to compute.

zero_center

If True, compute standard PCA from covariance matrix. If False, omit zero-centering variables (uses TruncatedSVD), which allows to handle sparse input efficiently. Passing None decides automatically based on sparseness of the data.

svd_solver

SVD solver to use:

'arpack'

for the ARPACK wrapper in SciPy (svds())

'randomized'

for the randomized algorithm due to Halko (2009).

'auto' (the default)

chooses automatically depending on the size of the problem.

random_state

Change to use different initial states for the optimization.

return_info

Only relevant when not passing an AnnData: see “Returns”.

use_highly_variable

Whether to use highly variable genes only, stored in .var['highly_variable']. By default uses them if they have been determined beforehand.

dtype

Numpy data type string to which to convert the result.

copy

If an AnnData is passed, determines whether a copy is returned. Is ignored otherwise.

chunked

If True, perform an incremental PCA on segments of chunk_size. The incremental PCA automatically zero centers and ignores settings of random_seed and svd_solver. If False, perform a full PCA.

chunk_size

Number of observations to include in each chunk. Required if chunked=True was passed.

Returns

X_pcascipy.sparse.spmatrix or numpy.ndarray

If data is array-like and return_info=False was passed, this function only returns X_pca…

adataAnnData

…otherwise if copy=True it returns or else adds fields to adata:

.obsm['X_pca']

PCA representation of data.

.varm['PCs']

The principal components containing the loadings.

.uns['pca']['variance_ratio'])

Ratio of explained variance.

.uns['pca']['variance']

Explained variance, equivalent to the eigenvalues of the covariance matrix.