Joint analysis of scATAC-seq datasets using epiConv

doi:10.1186/s12859-022-04858-w

	Joint analysis of scATAC-seq datasets using epiConv
	Lin, Li; Zhang, Liye
	2022-12
发表期刊	BMC BIOINFORMATICS (IF:2.9[JCR-2023],3.6[5-Year])
EISSN	1471-2105
卷号	23 期号:1
发表状态	已发表
DOI	10.1186/s12859-022-04858-w
摘要	Background: Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information. Results: Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable. Conclusions: In this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset. © 2022, The Author(s).
关键词	Cell culture Batch effect Cell proliferation Biological signals Chromosomes Cell clustering Depth profiling High-throughput Joint analysis Multiple data sets Multiple source ScATAC-seq Single cells Technical improvement
URL	查看原文
收录类别	EI ; SCIE
语种	英语
出版者	BioMed Central Ltd
EI入藏号	20223112472830
EI主题词	Data integration
EI分类号	461.2 Biological Materials and Tissue Engineering；461.9 Biology；723.2 Data Processing and Image Processing；801 Chemistry
原始文献类型	Journal article (JA)
Scopus 记录号	2-s2.0-85135171725
来源库	Scopus
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/211740
专题	生命科学与技术学院_PI研究组_张力烨组生命科学与技术学院_PI研究组_孙建龙组
通讯作者	Lin, Li; Zhang, Liye
作者单位	School of Life Science and Technology,ShanghaiTech University,Shanghai,China
第一作者单位	生命科学与技术学院
通讯作者单位	生命科学与技术学院
第一作者的第一单位	生命科学与技术学院
推荐引用方式 GB/T 7714	Lin, Li,Zhang, Liye. Joint analysis of scATAC-seq datasets using epiConv[J]. BMC BIOINFORMATICS,2022,23(1).
APA	Lin, Li,&Zhang, Liye.(2022).Joint analysis of scATAC-seq datasets using epiConv.BMC BIOINFORMATICS,23(1).
MLA	Lin, Li,et al."Joint analysis of scATAC-seq datasets using epiConv".BMC BIOINFORMATICS 23.1(2022).