alphapepttools.pp.impute_gaussian

alphapepttools.pp.impute_gaussian#

alphapepttools.pp.impute_gaussian(adata, std_offset=3, std_factor=0.3, random_state=42, layer=None, *, copy=False)#

Impute missing values in each column by random sampling from a gaussian distribution.

The distribution is centered at std_offset * feature standard deviation below the feature mean and has a standard deviation of std_factor * feature standard deviation. The function returns a copy of the AnnData object with imputed values in place of NaNs.

Parameters:

  • adata (anndata.AnnData) – AnnData object containing the data to be imputed.

  • std_offset (float) – Number of standard deviations below the mean to center the gaussian distribution.

  • std_factor (float) – Factor to multiply the feature’s standard deviation with to get the standard deviation of the gaussian distribution.

  • layer (Optional[str] (default: None)) – Name of the layer to impute. If None (default), the data matrix X is used.

  • copy (bool (default: False)) – Whether to return a modified copy (True) of the anndata object. If False (default) modifies the object inplace

Return type:

AnnData

Returns:

None | anndata.AnnData AnnData object with imputed values in layer. If copy=False modifies the anndata object at layer inplace and returns None. If copy=True, returns a modified copy.

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