scib.metrics.pcr_comparison

scib.metrics.pcr_comparison(adata_pre, adata_post, covariate, embed=None, n_comps=50, scale=True, verbose=False)

Principal component regression score

Compare the explained variance before and after integration using pc_regression(). Return either the difference of variance contribution before and after integration or a score between 0 and 1 (scaled=True) with 0 if the variance contribution hasn’t changed. The larger the score, the more different the variance contributions are before and after integration.

Parameters

  • adata_pre – anndata object before integration

  • adata_post – anndata object after integration

  • covariate – Key for adata.obs column to regress against

  • embed – Embedding to use for principal components. If None, use the full expression matrix (adata.X), otherwise use the embedding provided in adata_post.obsm[embed].

  • n_comps – Number of principal components to compute

  • scale – If True, scale score between 0 and 1 (default)

  • verbose –

Returns

Difference of variance contribution of PCR (scaled between 0 and 1 by default)