scReg2 0.1.0

scReg2: integrate RNA and ATAC dimention reduction with batch remove

BETA

This package still in beta, please feel free to e-mail me for any question about the package and usage.

email: fchang[at]clemson.edu

FULL Tutorial

scReg2

Install

pip install screg2

Usage

Load data form h5ad file

If you have 2 anndata object that already finished scaling, normalizing,... You can just simply use screg2.RegNMF() to integrate rna and atac data, with batch remove. screg2.RegNMF() will return the rna anndata object with scReg_reduction in it rna_adata.obsm

import pandas as pd
import numpy as np
import scanpy as sc
import screg2 

rna_file = '/path/to/your/rna/h5ad/file'
atac_file = '/path/to/your/atac/h5ad/file'
rna_adata = sc.read_h5ad(rna_file)
atac_adata= sc.read_h5ad(atac_file)
rna_adata = screg2.RegNMF(rna_data=rna_adata, atac_data=atac_adata, Meta_data=rna_adata.obs,batch_type='sample', maxiter=100, key_added="scReg_reduction")

screg2.RegNMF() will return the rna anndata object with scReg_reduction in it rna_adata.obsm

rna_adata.obsm['scReg_reduction']

array([[4.13353097e-14, 1.08500913e-10, 3.27313772e-11, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
      [2.09097071e-14, 1.96949657e-16, 1.67404740e-11, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
      [1.12355639e-10, 3.41268614e-11, 6.89114276e-11, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
      ...,
      [6.38938638e-14, 7.88140180e-11, 5.65415185e-13, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
      [1.88311115e-10, 1.70259133e-10, 6.51093299e-12, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
      [2.15576528e-12, 9.40752216e-11, 1.57056569e-12, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])

Warning

If you do not have the meta data of cells for their sample infomation the nex code might help you

rna_adata.obs['sample'] = adata.obs_names.str.split("-").str[1]

Clustering and Umap

sc.pp.neighbors(rna_adata, use_rep='scReg_reduction', key_added='scReg_neighbors', n_neighbors=40)
sc.tl.leiden(rna_adata, neighbors_key='scReg_neighbors', key_added='scReg_leiden')
sc.tl.umap(rna_adata, neighbors_key='scReg_neighbors')
sc.pl.umap(rna_adata, color="scReg_leiden", legend_loc="on data",legend_fontsize="small",size=6, title="scReg")
# If you want to save the figure you can
# sc.pl.umap(rna_adata, color="scReg_leiden", legend_loc="on data",legend_fontsize="small",size=6, save="_scReg.pdf" title="scReg")