scib.metrics.pcr
- scib.metrics.pcr(adata, covariate, embed=None, n_comps=50, recompute_pca=True, verbose=False)
Principal component regression for anndata object
Wraps
pc_regression()
while checking whether to:compute PCA on embedding or expression data (set
embed
to name of embedding matrix e.g.embed='X_emb'
)use existing PCA (only if PCA entry exists)
recompute PCA on expression matrix (default)
- Parameters:
adata – Anndata object
covariate – Key for
adata.obs
column to regress againstembed – Embedding to use for principal component analysis. If None, use the full expression matrix (
adata.X
), otherwise use the embedding provided inadata.obsm[embed]
.n_comps – Number of PCs, if PCA is recomputed. The PCA will be recomputed if neither PCA loadings nor the principle components can be found.
recompute_pca – whether to recompute a PCA on the
- Returns:
Variance contribution of regression
The function can be computed on full corrected feature spaces and latent embeddings. No preprocessing is needed, as the function can perform PCA if
recompute_pca=True
. Alternatively, you can also provide precomputed PCA, if the principle components are saved under.obsm["X_pca"]
and the PC loadings are saved in.uns["pca"]["variance"]
.Examples
# full feature output scib.me.pcr(adata, covariate="batch", recompute_pca=True) # embedding output scib.me.pcr(adata, covariate="batch", embed="X_emb")