KMS Of Academy of mathematics and systems sciences, CAS
| 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
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| 2020-04-15 | |
| Source Publication | BIOINFORMATICS
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| ISSN | 1367-4803 |
| Volume | 36Issue:8Pages:2474-2485 |
| Abstract | 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 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
| WOS Subject | Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability |
| WOS ID | WOS:000537473400020 |
| Publisher | OXFORD UNIV PRESS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/51576 |
| Collection | 应用数学研究所 |
| Corresponding Author | Wang, Yong |
| Affiliation | 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 |
| 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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