snapatac2.pp.mnc_correct#

snapatac2.pp.mnc_correct(adata, *, batch, n_neighbors=5, n_clusters=40, n_iter=1, use_rep='X_spectral', use_dims=None, groupby=None, key_added=None, inplace=True, n_jobs=8)[source]#

A modified MNN-Correct algorithm based on cluster centroid.

Parameters:

data – Matrice or AnnData object. Matrices should be shaped like n_obs x n_vars.
batch – Batch labels for cells. If a string, labels will be obtained from obs.
n_neighbors – Number of mutual nearest neighbors.
n_clusters – Number of clusters
n_iter – Number of iterations.
use_rep – Use the indicated representation in .obsm.
use_dims – Use these dimensions in use_rep.
groupby – If specified, split the data into groups and perform batch correction on each group separately.
key_added – If specified, add the result to adata.obsm with this key. Otherwise, it will be stored in adata.obsm[use_rep + "_mnn"].
inplace – Whether to store the result in the anndata object.
n_jobs – Number of jobs to use for parallelization.

Returns:

if inplace=True it updates adata with the field adata.obsm[`use_rep`_mnn], containing adjusted principal components. Otherwise, it returns the result as a numpy array.

Return type:

np.ndarray | None