pegasus.pca¶
- pegasus.pca(data, n_components=50, features='highly_variable_features', standardize=True, max_value=10.0, n_jobs=- 1, random_state=0)[source]¶
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 indata.var
to specify features used for PCA.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. IfNone
, 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)