KMS Of Academy of mathematics and systems sciences, CAS
| Statistical test of structured continuous trees based on discordance matrix | |
| Bai, Xiangqi1,2; Ma, Liang3; Wan, Lin1,2 | |
| 2019-12-01 | |
| Source Publication | BIOINFORMATICS
 |
| ISSN | 1367-4803 |
| Volume | 35Issue:23Pages:4962-4970 |
| Abstract | Motivation: Cell fate determination is a continuous process in which one cell type diversifies to other cell types following a hierarchical path. Advancements in single-cell technologies provide the opportunity to reveal the continuum of cell progression which forms a structured continuous tree (SCTree). Computational algorithms, which are usually based on a priori assumptions on the hidden structures, have previously been proposed as a means of recovering pseudo trajectory along cell differentiation process. However, there still lack of statistical framework on the assessments of intrinsic structure embedded in high-dimensional gene expression profile. Inherit noise and cell-to-cell variation underlie the single-cell data, however, pose grand challenges to testing even basic structures, such as linear versus bifurcation. Results: In this study, we propose an adaptive statistical framework, termed SCTree, to test the intrinsic structure of a high-dimensional single-cell dataset. SCTree test is conducted based on the tools derived from metric geometry and random matrix theory. In brief, by extending the Gromov-Farris transform and utilizing semicircular law, we formulate the continuous tree structure testing problem into a signal matrix detection problem. We show that the SCTree test is most powerful when the signal-to-noise ratio exceeds a moderate value. We also demonstrate that SCTree is able to robustly detect linear, single and multiple branching events with simulated datasets and real scRNA-seq datasets. Overall, the SCTree test provides a unified statistical assessment of the significance of the hidden structure of single-cell data. |
| DOI | 10.1093/bioinformatics/btz425 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[11571349] ; National Natural Science Foundation of China[91630314] ; National Natural Science Foundation of China[81673833] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13050000] ; NCMIS of CAS ; LSC of CAS ; Youth Innovation Promotion Association of CAS |
| 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:000506808900013 |
| Publisher | OXFORD UNIV PRESS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/50529 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Ma, Liang; Wan, Lin |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Beijing Inst Genom, CAS Key Lab Genom & Precis Med, Beijing 100101, Peoples R China |
| Recommended Citation GB/T 7714 | Bai, Xiangqi,Ma, Liang,Wan, Lin. Statistical test of structured continuous trees based on discordance matrix[J]. BIOINFORMATICS,2019,35(23):4962-4970. |
| APA | Bai, Xiangqi,Ma, Liang,&Wan, Lin.(2019).Statistical test of structured continuous trees based on discordance matrix.BIOINFORMATICS,35(23),4962-4970. |
| MLA | Bai, Xiangqi,et al."Statistical test of structured continuous trees based on discordance matrix".BIOINFORMATICS 35.23(2019):4962-4970. |
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