stlearn.em.run_ica

stlearn.em.run_ica(adata: anndata._core.anndata.AnnData, n_factors: int = 20, fun: str = 'logcosh', tol: float = 0.0001, use_data: str = None, copy: bool = False) → Optional[anndata._core.anndata.AnnData]

FastICA: a fast algorithm for Independent Component Analysis.

Parameters

• adata – Annotated data matrix.

• n_factors – Number of components to use. If none is passed, all are used.

• fun –

  The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. Example: def my_g(x):

  return x ** 3, (3 * x ** 2).mean(axis=-1)

• tol – Tolerance on update at each iteration.

• use_data – if None, then using all the gene expression profile. Else, use the chosen data from adata.obsm.

• copy – Return a copy instead of writing to adata.

Returns

• Depending on copy, returns or updates adata with the following fields.

• `X_ica` (numpy.ndarray (adata.obsm)) – Independent Component Analysis representation of data.