omicverse.pp.normalize_pearson_residuals

omicverse.pp.normalize_pearson_residuals#

omicverse.pp.normalize_pearson_residuals(adata, *, theta=100, clip=None, check_values=True, layer=None, inplace=True, copy=False, **kwargs)[source]#

Normalize a count matrix using analytic Pearson residuals (Lause 2021).

Parameters:

  • adata (anndata.AnnData) – AnnData object containing counts in adata.X (or layer).

  • theta (float (default: 100)) – Negative binomial overdispersion parameter. 100 matches Lause 2021 and is a reasonable default for scRNA.

  • clip (float | None (default: None)) – Clip residuals to [-clip, clip]. None uses the scanpy default (sqrt(n_obs)).

  • check_values (bool (default: True)) – Validate that the input looks like counts.

  • layer (str | None (default: None)) – Layer to normalise. None uses adata.X.

  • inplace (bool (default: True)) – Write back into adata (True) or return the matrix (False).

  • copy (bool (default: False)) – Return a new AnnData instead of modifying in place (only when inplace=True).

  • **kwargs – Forwarded to scanpy.experimental.pp.normalize_pearson_residuals.

Returns:

Updates adata.X in place with Pearson residuals.

Return type:

None

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