Epi-Impute

Introduction

This repository contains primary source code for "Epi-Impute: single-cell RNA-seq imputation via integration with single-cell ATAC-seq" that is a computational tool for imputing scRNA-seq data from DNA accessibility data (scATAC-seq) from consistent cell-type populations.

Epi-Impute exploits the idea of open chromatin in active cis-regulatory elements of the genes and estimates average accessibility of gene regulatory elements, e.g., promoters and enhancers, in order to add a binarized pseudo-count to the gene expression values reflecting the potential for transcription activation observed at the epigenetic level

Preprocessing

For preprocessing of scRNA-seq data, please follow the standard processing pipeline to get the expression count matrix, where each row represents a gene and each column represents a cell. Epi-Impute supports both raw and normalized data.

For scATAC-seq data, please, obtain a count matrix and annotations with preprocessing pipeline you are using.

Requirements

Epi-Impute requires R version 4.0.0 or above and following packages:

readr
reshape2
feather
rlist
data.table
RCurl
MACS
UMAP (https://github.com/lmcinnes/umap)

Installation

Install from Github

library(devtools)
install_github("raevskymichail/epi-impute/epi.impute")

Install from source codes

Download source codes and then type in R session:

install.packages(path_to_archive, type = "source", rep = NULL)

Where path_to_archive would represent the full path and file name:

On Windows it will look something like this: C:\\Downloads\epi-impute.tar.gz.
On UNIX machines it will look like this: ~/Downloads/epi-impute.tar.gz.

Quick start

library("epi.impute")

data <- load_example_data()

data_imputed <- epi_impute(sc_exp_data = data[["sc_exp_data"]],
                           sc_atac_data = data[["sc_atac_data"]],
                           sc_atac_cell_names = data[["sc_atac_cell_names"]],
                           sc_atac_peaks_ann = data[["sc_atac_peaks_ann"]],
                           cell_types = c("HSC", "CMP", "GMP"))

Where

sc_exp_data – scRNA-seq count matrix, where rownames are HGNC genes (HUGO) and colnames are cell ids

sc_atac_data – scATAC-seq count matrix, where rownames are cell ids and colnames are ids for euchromatine peaks (obtained from peak caller, for ex. MACS2)

sc_atac_cell_names – matrix, containing description and annotation for cell types observed in scATAC-seq count matrix. It should have rownames (cell ids) that match rownames of sc_atac_data

sc_atac_peaks_ann – matrix with annotations (coordinates) for euchromatine peaks, presented in the scATAC-seq count matrix. It should have rownames (peak ids) that match colnames of sc_atac_data

cell_types – vector, containing names for cell types, presented in count matrix.

atac_bin_thrld – numeric value for accessibility threshold used for primary binirization of peaks in scATAC-seq matrix.

Help

Please feel free to contact Mikhail Raevskiy (raevskii.mm@phystech.edu) if you have any questions about the software.