Robust enhancer-gene association prediction using single cell transcriptomes and epigenomes

This repository hosts customized scripts for the analysis of enhancer-gene associations in neurons from mouse primary motor cortex.

Reference:

Xie, Armand et al. 2021; Robust enhancer-gene regulation identified by single-cell transcriptomes and epigenomes

Correspondence: Fangming Xie and Eran A. Mukamel

Getting started

System requirements

This package is tested on a Ubuntu 18.04.6 LTS (Bionic Beaver) server. However, we expect it can be operated under a wide range of systems. We recommend users to use a conda environment to install dependencies. This requires users to pre-install Anaconda.

Installation

Run the following command to clone this repo and install dependencies.

# clone this repo
git clone https://github.com/mukamel-lab/SingleCellFusion_EnhancerPaper.git

# create an conda env and install dependancies.
cd SingleCellFusion_EnhancerPaper
conda env create -f env.yml

The installation of the conda environment takes less than 20 minutes. After installation, activate the environment using

conda activate env_enhancer

Demo

First download demo data, which includes mC, ATAC, and RNA profiles for >70,000 neurons from mouse primary motor cortex:

Use this link to download.

Put data.tar.gz under the demo directory of this repo, and decompress it:

mv data.tar.gz ./demo
tar -zxvf data.tar.gz

Now you can run our enhancer-gene association analysis using a single command:

cd demo
 ./run_pipe_mc.sh & ./run_pipe_atac.sh

This will generate a result folder that includes metacells, kNN graphs between modalities, and the correlations of all enhancer-gene pairs within 1Mb for mC-RNA and ATAC-RNA, respectively. For speed, this demo uses only 10% of cells for each dataset based on random sampling.

To visualize the results, then run through the visualize_correlation.ipynb jupyter notebook. This will generate results that recapitulate the key findings of the paper.

The whole demo takes about 5 minutes to run through.

Run your data

You need to replace our example data folder demo/data with your own data. As in demo/data, you would need:

a gene list
an enhancer list
a list of enhancer-gene pairs to examine (default uses all pairs within 1Mbp)
count matrices (cell-by-gene for RNA; cell-by-enhancer for mC and ATAC)
feature matrices (cell-by-gene for RNA, mC and ATAC; preprocessed and normalized; this is used to generate metacells)