Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data.

Xiao Huo,Chengchen Zhao,Yong Zhang,Sheng’en Hu

doi:10.1371/journal.pone.0180583

Abstract

An increasing number of single cell transcriptome and epigenome technologies, including single cell ATAC-seq (scATAC-seq), have been recently developed as powerful tools to analyze the features of many individual cells simultaneously. However, the methods and software were designed for one certain data type and only for single cell transcriptome data. A systematic approach for epigenome data and multiple types of transcriptome data is needed to control data quality and to perform cell-to-cell heterogeneity analysis on these ultra-high-dimensional transcriptome and epigenome datasets. Here we developed Dr.seq2, a Quality Control (QC) and analysis pipeline for multiple types of single cell transcriptome and epigenome data, including scATAC-seq and Drop-ChIP data. Application of this pipeline provides four groups of QC measurements and different analyses, including cell heterogeneity analysis. Dr.seq2 produced reliable results on published single cell transcriptome and epigenome datasets. Overall, Dr.seq2 is a systematic and comprehensive QC and analysis pipeline designed for parallel single cell transcriptome and epigenome data. Dr.seq2 is freely available at: http://www.tongji.edu.cn/~zhanglab/drseq2/ and https://github.com/ChengchenZhao/DrSeq2.

Highlights

To better understand cell-to-cell variability, an increasing number of transcriptome technologies, such as Drop-seq [1, 2], Cyto-seq [3], 10x genomics [4], MARS-seq [5], and epigenome technologies, such as Drop-ChIP [6], single cell ATAC-seq [7], have been developed in recent years
The Dr.seq2 Quality Control (QC) and analysis pipeline is suitable for both single cell transcriptome data and epigenome data
Dr.seq2 is designed for QC and analysis components of parallel single cell transcriptome and epigenome data

Summary

Introduction

To better understand cell-to-cell variability, an increasing number of transcriptome technologies, such as Drop-seq [1, 2], Cyto-seq [3], 10x genomics [4], MARS-seq [5], and epigenome technologies, such as Drop-ChIP [6], single cell ATAC-seq (scATAC-seq) [7], have been developed in recent years. These technologies can provide a large amount of single cell transcriptome information or epigenome information at minimal cost, which makes it possible to perform analysis of cell heterogeneity on the transcriptome and epigenome levels, deconstruction of a cell population, and detection of rare cell populations. The methods and software such as Dr.seq [8] were developed for one single cell data type with certain functions

Methods

Results

Conclusion