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scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization
Feng, Zhanying1,2; Ren, Xianwen3; Fang, Yuan4; Yin, Yining4; Huang, Chutian4; Zhao, Yimin4; Wang, Yong1,2,5
Source PublicationBIOINFORMATICS
AbstractMotivation: 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.
Indexed BySCI
Funding ProjectStrategic 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 Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000537473400020
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Document Type期刊论文
Corresponding AuthorWang, Yong
Affiliation1.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
Recommended Citation
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.
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