stlearn.em.run_pca

stlearn.em.run_pca(data: Union[AnnData, ndarray, spmatrix], n_comps: int = 50, zero_center: Optional[bool] = True, svd_solver: str = 'auto', random_state: Optional[Union[int, RandomState]] = 0, return_info: bool = False, use_highly_variable: Optional[bool] = None, dtype: str = 'float32', copy: bool = False, chunked: bool = False, chunk_size: Optional[int] = None) → Union[AnnData, ndarray, spmatrix][source]

Wrap function scanpy.pp.pca Principal component analysis [Pedregosa11]. Computes PCA coordinates, loadings and variance decomposition. Uses the implementation of scikit-learn [Pedregosa11]. :param data: The (annotated) data matrix of shape n_obs × n_vars.

Rows correspond to cells and columns to genes.

Parameters:

  • 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_pca (spmatrix, ndarray) – If data is array-like and return_info=False was passed, this function only returns X_pca…

  • adata (anndata.AnnData) – …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.