scanpy.pp.scale

scanpy.pp.scale(X, zero_center=True, max_value=None, copy=False, layer=None, obsm=None)
scanpy.pp.scale(X: numpy.ndarray, *, zero_center: bool = True, max_value: Optional[float] = None, copy: bool = False, return_mean_std: bool = False)
scanpy.pp.scale(X: scipy.sparse.base.spmatrix, *, zero_center: bool = True, max_value: Optional[float] = None, copy: bool = False, return_mean_std: bool = False)

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 (for zero_center==True) set to 0 during this operation. In the future, they might be set to NaNs.

Parameters
X : AnnData | spmatrix | ndarrayUnion[AnnData, spmatrix, ndarray]

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

zero_center : boolbool (default: True)

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

max_value : float | NoneOptional[float] (default: None)

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

copy : boolbool (default: False)

Whether this function should be performed inplace. If an AnnData object is passed, this also determines if a copy is returned.

layer : str | NoneOptional[str] (default: None)

If provided, which element of layers to scale.

obsm : str | NoneOptional[str] (default: None)

If provided, which element of obsm to scale.

Returns

Depending on copy returns or updates adata with a scaled adata.X, annotated with 'mean' and 'std' in adata.var.