stlearn.pp.scale

stlearn.pp.scale(adata: Union[anndata._core.anndata.AnnData, numpy.ndarray, scipy.sparse.base.spmatrix], zero_center: bool = True, max_value: Optional[float] = None, copy: bool = False) → Optional[anndata._core.anndata.AnnData][source]

Wrap function of scanpy.pp.scale

Scale data to unit variance and zero mean. .. note:

Variables (genes) that do not display any variation (are constant across
all observations) are retained and set to 0 during this operation. In
the future, they might be set to NaNs.
Parameters

  • data – The (annotated) data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.

  • zero_center – If False, omit zero-centering variables, which allows to handle sparse input efficiently.

  • max_value – Clip (truncate) to this value after scaling. If None, do not clip.

  • copy – If an AnnData is passed, determines whether a copy is returned.

Returns

Return type

Depending on copy returns or updates adata with a scaled adata.X.