ResPAN

This reporsity contains code and information of data used in the paper “ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks”. Source code for ResPAN are in the ResPAN folder, scipts for reproducing benchmarking results are in the scripts folder, and data information can be found in the data folder.

ResPAN is a light structured Residual autoencoder and mutual nearest neighbor Paring guided Adversarial Network for scRNA-seq batch correction. The workflow of ResPAN contains three key steps: generation of training data, adversarial training of the neural network, and generation of corrected data without batch effect. A figure summary is shown below.

More details about ResPAN can be found in our manuscript.

Package requirement

ResPAN is implemented in Python and based on the framework of PyTorch. Before downloading and installing ResPAN, some packages need to be installed first. These required packages along with their versions used in our manuscript are listed below.

Package	Version
numpy	1.18.1
pandas	1.3.5
scipy	1.8.0
scanpy	1.8.2
pytorch	1.10.2+cu113

Installation

There are two ways to install and use ResPAN, the first and easiest way is to use pip install:

pip install ResPAN

If it doesn't work, you can also make a clone of this GitHub repository:

git clone https://github.com/AprilYuge/ResPAN.git

A callable function run_respan is in ResPAN/respan.py.

Brief tutorial

A brief tutorial of using ResPAN can be found below and under the folder tutorials.

To run our method, the first thing is to import necessary packages:

import numpy as np
import pandas as pd
import scanpy as sc
import scipy
from ResPAN import run_respan

Then we need to load the scRNA-seq data with batch information and preprocess it before running ResPAN:

# data loading
adata = sc.read_loom('CL_raw.loom', sparse=False) 
# pre-processing
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
sc.pp.normalize_per_cell(adata, counts_per_cell_after=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key='batch')
adata = adata[:, adata.var['highly_variable']]
# check if data is in sparse format
if isinstance(adata.X, scipy.sparse.csr.csr_matrix): 
    adata_new = sc.AnnData(adata.X.todense())
    adata_new.obs = adata.obs.copy()
    adata_new.obs_names = adata.obs_names
    adata_new.var_names = adata.var_names
    adata_new.obs_names.name = 'CellID'
    adata_new.var_names.name = 'Gene'
    del adata
    adata = adata_new

Now we can run ResPAN on the preprocessed data for batch correction. The output result is an AnnData object:

adata_new = run_respan(adata, batch_key='batch', epoch=300, batch=1024, reduction='pca', subsample=3000, seed=999)

As indicated in our manuscipt, we use PCA for dimensionality reduction, kPCA (reduction='kpca') and CCA (reduction='cca') are also implemented, but their performance were not as good as PCA. Meanwhile, we subsampled cells in each batch to 3,000 before finding random walk MNN pairs [1].

To visualize our results, we can use the following commands:

adata_new.raw = adata_new
sc.pp.scale(adata_new, max_value=10)
sc.tl.pca(adata_new, 20, svd_solver='arpack')
sc.pp.neighbors(adata_new)
sc.tl.umap(adata_new)
sc.set_figure_params(figsize=(5,5),fontsize=12)
sc.pl.umap(adata_new, color=['batch', 'celltype'], frameon=False, show=False)

Code references

For the implementation of ResPAN, we referred to WGAN-GP for the structure of Generative Adversarial Network and iMAP for the random walk mutual nearest neighbor method. Many thanks to their open-source treasure.

API document

For the API of our model, please refer this link.

Paper references

[1] Wang, Dongfang, et al. "iMAP: integration of multiple single-cell datasets by adversarial paired transfer networks." Genome biology 22.1 (2021): 1-24.