scib.metrics.pc_regression

scib.metrics.pc_regression(data, covariate, pca_var=None, n_comps=50, svd_solver='arpack', verbose=False)

Principal component regression

Compute the overall variance contribution given a covariate according to the following formula:

\[Var(C|B) = \sum^G_{i=1} Var(C|PC_i) \cdot R^2(PC_i|B)\]

for \(G\) principal components (\(PC_i\)), where \(Var(C|PC_i)\) is the variance of the data matrix \(C\) explained by the i-th principal component, and \(R^2(PC_i|B)\) is the \(R^2\) of the i-th principal component regressed against a covariate \(B\).

Parameters:

  • data – Expression or PC matrix. Assumed to be PC, if pca_sd is given.

  • covariate – series or list of batch assignments

  • n_comps – number of PCA components for computing PCA, only when pca_sd is not given. If no pca_sd is not defined and n_comps=None, compute PCA and don’t reduce data

  • pca_var – Iterable of variances for n_comps components. If pca_sd is not None, it is assumed that the matrix contains PC, otherwise PCA is computed on data.

  • svd_solver –

  • verbose –

Returns:

Variance contribution of regression