pegasus.pca

pegasus.pca(data, n_components=50, features='highly_variable_features', standardize=True, max_value=10.0, n_jobs=-1, random_state=0)

Perform Principle Component Analysis (PCA) to the data.

The calculation uses scikit-learn implementation.

Parameters

• data (pegasusio.MultimodalData) – Annotated data matrix with rows for cells and columns for genes.

• n_components (int, optional, default: 50.) – Number of Principal Components to get.

• features (str, optional, default: "highly_variable_features".) – Keyword in data.var to specify features used for PCA. If None, all features will be selected.

• standardize (bool, optional, default: True.) – Whether to scale the data to unit variance and zero mean.

• max_value (float, optional, default: 10.) – The threshold to truncate data after scaling. If None, do not truncate.

• n_jobs (int, optional (default: -1)) – Number of threads to use. -1 refers to using all physical CPU cores.

• random_state (int, optional, default: 0.) – Random seed to be set for reproducing result.

Return type

None

Returns

• None.

• Update data.obsm –

  • data.obsm["X_pca"]: PCA matrix of the data.

• Update data.uns –

  • data.uns["PCs"]: The principal components containing the loadings.

  • data.uns["pca_variance"]: Explained variance, i.e. the eigenvalues of the covariance matrix.

  • data.uns["pca_variance_ratio"]: Ratio of explained variance.

  • data.uns["pca_features"]: Record the features used to generate PCA components.

Examples

>>> pg.pca(data)