Statistical test of structured continuous trees based on discordance matrix
Bai, Xiangqi1,2; Ma, Liang3; Wan, Lin1,2
Source PublicationBIOINFORMATICS
AbstractMotivation: 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.
Indexed BySCI
Funding ProjectNational 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 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:000506808900013
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Document Type期刊论文
Corresponding AuthorMa, Liang; Wan, Lin
Affiliation1.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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