stlearn.em.run_pca

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

Wrap function scanpy.pp.pca

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’ (the default - deterministic) for the ARPACK wrapper in SciPy (svds())

    • ’randomized’ for the randomized algorithm due to Halko (2009).

    • ’auto’ 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.