Approximate distance correlation for selecting highly interrelated genes across datasets

doi:10.1371/journal.pcbi.1009548

CSpace > 应用数学研究所

	Approximate distance correlation for selecting highly interrelated genes across datasets
	Shen, Qunlun1,2 ; Zhang, Shihua1,2,3,4
	2021-11-01
发表期刊	PLOS COMPUTATIONAL BIOLOGY
ISSN	1553-734X
卷号	17 期号:11 页码:18
摘要	With the rapid accumulation of biological omics datasets, decoding the underlying relationships of cross-dataset genes becomes an important issue. Previous studies have attempted to identify differentially expressed genes across datasets. However, it is hard for them to detect interrelated ones. Moreover, existing correlation-based algorithms can only measure the relationship between genes within a single dataset or two multi-modal datasets from the same samples. It is still unclear how to quantify the strength of association of the same gene across two biological datasets with different samples. To this end, we propose Approximate Distance Correlation (ADC) to select interrelated genes with statistical significance across two different biological datasets. ADC first obtains the k most correlated genes for each target gene as its approximate observations, and then calculates the distance correlation (DC) for the target gene across two datasets. ADC repeats this process for all genes and then performs the Benjamini-Hochberg adjustment to control the false discovery rate. We demonstrate the effectiveness of ADC with simulation data and four real applications to select highly interrelated genes across two datasets. These four applications including 21 cancer RNA-seq datasets of different tissues; six single-cell RNA-seq (scRNA-seq) datasets of mouse hematopoietic cells across six different cell types along the hematopoietic cell lineage; five scRNA-seq datasets of pancreatic islet cells across five different technologies; coupled single-cell ATAC-seq (scATAC-seq) and scRNA-seq data of peripheral blood mononuclear cells (PBMC). Extensive results demonstrate that ADC is a powerful tool to uncover interrelated genes with strong biological implications and is scalable to large-scale datasets. Moreover, the number of such genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies. Author summaryThe number and size of biological datasets (e.g., single-cell RNA-seq datasets) are booming recently. How to mine the relationships of genes across datasets is becoming an important issue. Computational tools of identifying differentially expressed genes have been comprehensively studied, but the interrelated genes across datasets are always neglected. Detecting of highly interrelated genes across datasets is hindered because the samples of them are always different and they could have different numbers of samples. To solve this problem, we present a new algorithm that can identify interrelated genes across datasets based on distance correlation. Our proposed algorithm is very efficient and works well in different technologies, i.e., RNA-seq, single-cell RNA-seq and single-cell ATAC-seq. Also, we found that the number of such highly interrelated genes can serve as a metric to measure the similarity between two datasets, which could characterize the relative difference of diverse cell types and technologies.
DOI	10.1371/journal.pcbi.1009548
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2019YFA0709501] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDPB17] ; Key-Area Research and Development of Guangdong Province[2020B1111190001] ; National Natural Science Foundation of China[61621003] ; National Ten Thousand Talent Program for Young Top-notch Talents ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008] ; Shanghai Municipal Science and Technology Major Project[2017SHZDZX01]
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS记录号	WOS:000721101000005
出版者	PUBLIC LIBRARY SCIENCE
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59635
专题	应用数学研究所
通讯作者	Zhang, Shihua
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, CEMS,NCMIS, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming, Yunnan, Peoples R China 4.Chinese Acad Sci, Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Key Lab Syst Biol, Hangzhou, Peoples R China
推荐引用方式 GB/T 7714	Shen, Qunlun,Zhang, Shihua. Approximate distance correlation for selecting highly interrelated genes across datasets[J]. PLOS COMPUTATIONAL BIOLOGY,2021,17(11):18.
APA	Shen, Qunlun,&Zhang, Shihua.(2021).Approximate distance correlation for selecting highly interrelated genes across datasets.PLOS COMPUTATIONAL BIOLOGY,17(11),18.
MLA	Shen, Qunlun,et al."Approximate distance correlation for selecting highly interrelated genes across datasets".PLOS COMPUTATIONAL BIOLOGY 17.11(2021):18.