RegNMF

screg2.RegNMF(rna_data, atac_data, batch_type, Meta_data=None, K=100, feature_cutperc=0.01, key_added='scReg_reduction', maxiter=100, copy='rna', TFIDF=False, normalize=False)

Perform Coupled Non-negative Matrix Factorization (NMF) on RNA and ATAC data.

Parameters:

rna_dataAnnData

Annotated data for RNA.

atac_dataAnnData

Annotated data for ATAC.

batch_typestr

Type of batch information in Meta_data.

Meta_datapd.DataFrame, optional

Metadata containing batch information. Default is None.

Kint, optional

Number of components for NMF. Default is 100.

feature_cutpercfloat, optional

Feature cutoff precision. Default is 0.01.

key_addedstr, optional

Key to store results in rna_data.obsm. Default is “scReg_reduction”.

maxiterint, optional

Maximum number of iterations for NMF. Default is 40.

copystr, optional

Specify ‘rna’ or ‘atac’ to choose which data to copy results into. Default is ‘rna’.

Returns:

AnnData

Annotated data with NMF results added.

screg2.RegNMF_Matrix(E, O, Meta_data, batch_type, K=100, feature_cutperc=0.01, maxiter=40, TFIDF=True, normalize=True)

Perform Coupled Non-negative Matrix Factorization (NMF) on input matrices.

Parameters:

Ecsr_matrix

Sparse matrix for RNA data.

Ocsr_matrix

Sparse matrix for ATAC data.

Meta_datapd.DataFrame

Metadata containing batch information.

batch_typestr

Type of batch information in Meta_data.

Kint, optional

Number of components for NMF. Default is 100.

feature_cutpercfloat, optional

Feature cutoff precision. Default is 0.01.

maxiterint, optional

Maximum number of iterations for NMF. Default is 40.

Returns:

dict

Dictionary containing NMF results.

screg2.RegNMF_h5(h5_file, barcodes=None)

Perform Coupled Non-negative Matrix Factorization (NMF) on RNA and ATAC data from h5 file.

Parameters:

h5_filestr

Single cell multiome h5 file

barcodesstr

The barcodes of cells that you want use

Returns:

AnnData

Annotated data with NMF results added and normalized layer.

screg2.tfidf(X)

Compute tfidf for matrix X (cell X gene) https://github.com/stuart-lab/signac/blob/HEAD/R/preprocessing.R