scMVP - single cell multi-view processor

scMVP is a python toolkit for joint profiling of scRNA and scATAC data profiling and analysis with multi-modal VAE model.

Installation

Environment requirements:
scMVP requires Python3.7.x and Pytorch.
For example, use miniconda to install python and pytorch of CPU or GPU version.

conda install -c pytorch python=3.7 pytorch
# if you do not have jupyter notebook/ipython notebook, you can also install by conda
conda install jupyter

Then you can install scMVP from github repo:

# first move to your target directory
git clone https://github.com/bm2-lab/scMVP.git
cd scMVP/
python setup.py install

Try import scMVP in your python console and start your first tutorial with scMVP!

Data preparation

Your should first prepare your input files, example is as follows:

1. "XX_cell.tsv": cell barcodes of RNA
   
2. "XX_gene.count.mtx" or "XX_gene.count.tsv": gene expression matrix
   
3. "XX_cDNA.genes.tsv": gene names
   
4. "XX_cell.ATAC.tsv": cell barcodes of ATAC
   
5. "XX_chromatin.count.mtx" or "XX_chromatin.count.tsv": atac expression matrix
   
6. "XX_peak.tsv": peak names/ids
   

Bulit in dataset:

• dataset.scienceDataset(): sci-CAR paper dataset.
  
• dataset.pairedSeqDataset(): Paired-seq paper dataset.
  
• dataset.snareDataset(): SNARE-seq dataset.
  

Optional:

• "XX_embeddings.xls": given cell annotation labels.
  

User tutorial

1. Using scMVP for sci-CAR cell line mixture. demo

• Basic analysis modules with multi-VAE.

2. Using scMVP for snare-seq mouse cerebral cortex P0 dataset. demo

• Perform CRE-gene analysis with PLS-regression.

3. Using scMVP on customize joint profiling dataset.demo

• Load and analyze your own data.

Reference

scMVP: an integrative generative model for joint profiling of single cell RNA-seq and ATAC-seq data. 2020