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_post.obs column to regress against

  • embed – Embedding to use for principal component analysis. If None, use the full expression matrix (adata_post.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)

The function can be computed on full corrected feature spaces and latent embeddings for both integrated and unintegrated anndata.Anndata objects. No preprocessing is needed, as the function will perform PCA directly on the feature or embedding space.

Examples

# full feature output
scib.me.pcr_comparison(adata_unintegrated, adata, covariate="batch")

# embedding output
scib.me.pcr_comparison(adata_unintegrated, adata, covariate="batch", embed="X_emb")