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

文献类型：期刊论文

作者Feng, Zhanying1,2; Ren, Xianwen3; Fang, Yuan4; Yin, Yining4; Huang, Chutian4; Zhao, Yimin4; Wang, Yong1,2,5
刊名BIOINFORMATICS
出版日期2020-04-15
卷号36期号:8页码:2474-2485
ISSN号1367-4803
DOI10.1093/bioinformatics/btz936
英文摘要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.
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
语种英语
WOS记录号WOS:000537473400020
出版者OXFORD UNIV PRESS
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/51575]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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