scCompReg

scCompReg (Single-Cell Comparative Regulatory analysis) is an R package that provides coupled clustering and joint embedding of scRNA-seq and scATAC-seq on one sample, and performs comparative gene regulatory analysis between two conditions.

Please check the man page via ?function (for example, ?sc_compreg) for a detailed description of the types of inputs and outputs.

System Requirements

• Operating System: Linux or MacOS
• R (>= 3.6.0)
• Bedtools (Linux)
• Homer (Linux)

Change Log

v1.0.0

• scCompReg first release.

Intallation

Use the following command to install scCompReg R package from source code:

require(devtools)
devtools::install_github("SUwonglab/sc-compReg", ref="master", subdir="R_package")

Example

For a full example of using the scCompReg method, please refer to example.R. The necessary data have been uploaded to the data folder in this repository.

To download the data, make sure you have git lfs installed. Installation instructions can be found here: https://github.com/git-lfs/git-lfs/wiki/Installation

Next, run the following line in shell:

git lfs clone https://github.com/SUwonglab/sc-compReg.git

The downloaded data directory will be in sc-compReg/data/. Simply set in R

path = './example_data/'
prior_data_path = './prior_data/'

To run scCompReg, run the following lines in R:

library(scCompReg)

sample1 = readRDS(paste(path, 'sample1.rds', sep = ''))
sample2 = readRDS(paste(path, 'sample2.rds', sep = ''))

peak.name.intersect.dir = paste(path, 'PeakName_intersect.txt', sep='')
peak.gene.prior.dir = paste(path, 'peak_gene_prior_intersect.bed', sep='')
motif = readRDS(paste(prior_data_path, 'motif_human.rds', sep=''))
motif.file = readRDS(paste(path, 'motif_file.rds', sep=''))

compreg.output = sc_compreg(sample1$O1,
                            sample1$E1,
                            sample1$O1.idx,
                            sample1$E1.idx,
                            sample1$symbol1,
                            sample1$peak.name1,
                            sample2$O2,
                            sample2$E2,
                            sample2$O2.idx,
                            sample2$E2.idx,
                            sample2$symbol2,
                            sample2$peak.name2,
                            motif$motif.name,
                            motif$motif.weight,
                            motif$match2,
                            motif.file,
                            peak.name.intersect.dir,
                            peak.gene.prior.dir,
                            sep.char=' ')

To save the obtained output, run the lines below in R:

for (i in 1:compreg.output$n.pops) {
    write.table(compreg.output$hub.tf[[i]],
                paste(path, 'tf_', i, '.txt', sep=''),
                row.names = F,
                quote = F,
                sep = '\t')
    write.table(compreg.output$diff.net[[i]],
                paste(path, 'diff_net_', i, '.txt', sep=''),
                row.names = F,
                quote = F,
                sep = '\t')
}

Full Workflow

The entire scCompReg workflow consists of three mandatory steps and one optional step.

1. Download the prior_data directory from github via git clone [email protected]:SUwonglab/sc-compReg.git.

2. Optional: obtaining cluster assignments from coupled nonnegative matrix factorization.

   • Preproces data for cnmf:

     • Obtain peak.bed file

     • In sc-compReg/preprocess_data/, run the following script:

           bash cnmf_process_data.sh path/to/peak.bed genome_version path/to/prior_data

       where genome_version is one of {hg19, hg38, mm9, mm10}, and prior_data is a folder downloaded in step 1.

     • Output:

       • peak_gene_coupling_matrix.txt

     • After loading peak.name and symbol, run the following script in R to convert peak_gene_coupling_matrix.txt to D, the coupling matrix, using the following code in R:

         D <- cnmf_load_coupling_matrix('peak_gene_coupling_matrix.txt'),
                                        peak.name,
                                        symbol)

     • cnmf_tsne allows users to visualize clustering outputs. Below are two example plots generated by cnmf_tsne:

           cnmf_tsne(cnmf.output$H1, cnmf.output$H2, path=path, save.plot=F, perplexity=100)

       

   • Run cnmf to get the cluster labels for sample 1 and sample 2. The cluster labels should be passed to sc_compreg as O1.idx, E1.idx, O2.idx, and E2.idx. For an example on how to run cnmf, please refer to cnmf_example.R

   • Note: It is not required to obtain cluster assignments using the coupled nonnegative matrix factorization workflow. The necessary input to scCompReg is some consistent cluster assignments in scRNA-seq and scATAC-seq.

3. Process data for scCompReg

   • Obtain peak_name1.txt and peak_name2.txt files containing the peak names of sample 1 and sample 2, respectively in bed format (chr \t start \t end but ignoring the spaces in the previous text)

   • In sc-compReg/preprocess_data/, run the following script:

         bash sc_compreg_process_data_.sh path/to/peak_name1.txt path/to/peak_name2.txt genome_version path/to/prior_data

     where genome_version is one of {hg19, hg38, mm9, mm10}, and prior_data is a folder downloaded in step 1.

   • Output:

     • PeakName_intersect.txt
     • peak_gene_prior_intersect.bed
     • MotifTarget.txt

4. Follow the tutorial on the sc_compreg function.

   • The necessary inputs to sc_compreg are
     • consistent cluster assignments in scRNA-seq and scATAC-seq (can be obtained from coupled nonnegative matrix factorization or obtained elsewhere)
     • log2-transformed gene expression matrices of samples 1 and 2
     • log2-transformed chromatin accessibility matrices of samples 1 and 2
     • symbol names of samples 1 and 2
   • Input peak.name.intersect.dir is the path to the PeakName_intersect.txt file generated in step 3.
   • Input peak.gene.prior.dir is the path to the peak_gene_prior_intersect.bed file generated in step 3.
   • Load the corresponding motif file for human in R via

         motif = readRDS('prior_data/motif_human.rds')

     or for mouse,

         motif = readRDS('prior_data/motif_mouse.rds')

   • Load motif.file in R via

         motif.file = mfbs_load(motif.target.dir)

     where motif.target.dir is the path to the MotifTarget.txt file generated in step 3.

Usage

scCompReg provides access to the following functions:

Command	Description
sc_compreg	Performs single-cell comparative regulatory analysis based on scRNA-seq and scATAC-seq data from two different conditions.
mfbs_load	Efficiently loads the motif_target file and returns an R list of the loaded objects.

Citation

[1] Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data Zhana Duren, Wenhui Sophia Lu, Joseph G. Arthur, Preyas Shah, Jingxue Xin, Francesca Meschi, Miranda Lin Li, Corey M. Nemec, Yifeng Yin, and Wing Hung Wong