ehrapy.preprocessing.log_norm#

ehrapy.preprocessing.log_norm(adata, vars=None, base=None, offset=1, copy=False)[source]#

Apply log normalization.

Computes \(x = \log(x + offset)\), where \(log\) denotes the natural logarithm unless a different base is given and the default \(offset\) is \(1\)

Parameters:

  • adata (AnnData) – AnnData object containing X to normalize values in. Must already be encoded using encode().

  • vars (Union[str, Sequence[str], None]) – List of the names of the numeric variables to normalize. If None all numeric variables will be normalized. Defaults to None.

  • base (Union[int, float, None]) – Numeric base for logarithm. If None the natural logarithm is used.

  • offset (int | float) – Offset added to values before computing the logarithm. Defaults to 1 .

  • copy (bool) – Whether to return a copy or act in place. Defaults to False .

Return type:

Optional[AnnData]

Returns:

AnnData object with normalized X. Also stores a record of applied normalizations as a dictionary in adata.uns[“normalization”].

Examples

>>> import ehrapy as ep
>>> adata = ep.dt.mimic_2(encoded=True)
>>> adata_norm = ep.pp.log_norm(adata, copy=True)