rapids_singlecell.pp.normalize_pearson_residuals

rapids_singlecell.pp.normalize_pearson_residuals(adata, *, theta=100, clip=None, check_values=True, layer=None, inplace=True)

Applies analytic Pearson residual normalization, based on Lause21. The residuals are based on a negative binomial offset model with overdispersion theta shared across genes. By default, residuals are clipped to sqrt(n_obs) and overdispersion theta=100 is used.

Parameters:

adata AnnData

AnnData object

theta float (default: 100)

The negative binomial overdispersion parameter theta for Pearson residuals. Higher values correspond to less overdispersion (var = mean + mean^2/theta), and theta=np.Inf corresponds to a Poisson model.

clip float | None (default: None)

Determines if and how residuals are clipped: If None, residuals are clipped to the interval [-sqrt(n_obs), sqrt(n_obs)], where n_obs is the number of cells in the dataset (default behavior). If any scalar c, residuals are clipped to the interval [-c, c]. Set clip=np.Inf for no clipping.

check_values bool (default: True)

If True, checks if counts in selected layer are integers as expected by this function, and return a warning if non-integers are found. Otherwise, proceed without checking. Setting this to False can speed up code for large datasets.

layer str | None (default: None)

Layer to use as input instead of X. If None, X is used.

inplace bool (default: True)

If True, update AnnData with results. Otherwise, return results. See below for details of what is returned.

Return type:

cp.ndarray | None

Returns:

If inplace=True, adata.X or the selected layer in adata.layers is updated with the normalized values. If inplace=False the normalized matrix is returned.