scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization

doi:10.1093/bioinformatics/btz936

CSpace > 应用数学研究所

	scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization
	Feng, Zhanying 1,2; Ren, Xianwen 3; Fang, Yuan 4; Yin, Yining 4; Huang, Chutian 4; Zhao, Yimin 4; Wang, Yong1,2,5
	2020-04-15
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	36 期号:8 页码:2474-2485
摘要	Motivation: Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. Results: We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene's ability to reconstruct cell-cell relationship and minimize gene redundancy by considering gene-gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM's advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as 'mouse cell marker atlas', which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining.
DOI	10.1093/bioinformatics/btz936
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of Chinese Academy of Science[XDB13000000] ; National Science Foundation of China[11871463] ; National Science Foundation of China[61671444] ; National Science Foundation of China[61621003] ; National Science Foundation of China[91730301] ; National Science Foundation of China[11661141019]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000537473400020
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/51576
专题	应用数学研究所
通讯作者	Wang, Yong
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, MDIS, CEMS,NCMIS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.Peking Univ, Sch Life Sci, Beijing 100871, Peoples R China 4.Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China 5.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
推荐引用方式 GB/T 7714	Feng, Zhanying,Ren, Xianwen,Fang, Yuan,et al. scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization[J]. BIOINFORMATICS,2020,36(8):2474-2485.
APA	Feng, Zhanying.,Ren, Xianwen.,Fang, Yuan.,Yin, Yining.,Huang, Chutian.,...&Wang, Yong.(2020).scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization.BIOINFORMATICS,36(8),2474-2485.
MLA	Feng, Zhanying,et al."scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization".BIOINFORMATICS 36.8(2020):2474-2485.