grassp.pp.impute_gaussian

impute_gaussian(data, width=0.3, distance=1.8, per_sample=True, random_state=0, inplace=True)

Impute missing values using a Gaussian distribution.

This function imputes missing values (zeros) in the data matrix using a Gaussian distribution. The parameters of the Gaussian are derived from the observed (non-zero) values, with the mean shifted downward by a specified number of standard deviations.

Parameters:

data AnnData

Annotated data matrix with proteins as observations (rows).

width float (default: 0.3)

Width of the Gaussian distribution used for imputation, as a fraction of the standard deviation of observed values. Default is 0.3.

distance float (default: 1.8)

Number of standard deviations below the mean of observed values to center the imputation distribution. Default is 1.8.

per_sample bool (default: True)

If True, calculate parameters separately for each sample (column). If False, use global parameters. Default is True.

random_state Union[int, RandomState, None] (default: 0)

Seed for random number generation. Default is 0.

inplace bool (default: True)

If True, modify data in place. If False, return a copy. Default is True.

Return type:

ndarray | None

Returns:

numpy.ndarray or None If inplace=False, returns the imputed data matrix. If inplace=True, returns None and modifies the input data.

Notes

This implements a simple but effective imputation strategy commonly used in proteomics data analysis. Missing values are assumed to be below detection limit and are imputed from a Gaussian distribution with parameters derived from the observed values but shifted downward.