stlearn.em.run_ica

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

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.