气候变化领域集成服务门户(CCPortal): Revealing lineage-related signals in single-cell gene expression using random matrix theory

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DOI	10.1073/pnas.1913931118
	Revealing lineage-related signals in single-cell gene expression using random matrix theory
	Nitzan M.; Brenner M.P.
发表日期	2021
ISSN	00278424
卷号	118 期号:11
英文摘要	Gene expression profiles of a cellular population, generated by single-cell RNA sequencing, contains rich information about biological state, including cell type, cell cycle phase, gene regulatory patterns, and location within the tissue of origin. A major challenge is to disentangle information about these different biological states from each other, including distinguishing from cell lineage, since the correlation of cellular expression patterns is necessarily contaminated by ancestry. Here, we use a recent advance in random matrix theory, discovered in the context of protein phylogeny, to identify differentiation or ancestry-related processes in single-cell data. Qin and Colwell [C. Qin, L. J. Colwell, Proc. Natl. Acad. Sci. U.S.A. 115, 690-695 (2018)] showed that ancestral relationships in protein sequences create a power-law signature in the covariance eigenvalue distribution. We demonstrate the existence of such signatures in scRNA-seq data and that the genes driving them are indeed related to differentiation and developmental pathways. We predict the existence of similar power-law signatures for cells along linear trajectories and demonstrate this for linearly differentiating systems. Furthermore, we generalize to show that the same signatures can arise for cells along tissuespecific spatial trajectories. We illustrate these principles in diverse tissues and organisms, including the mammalian epidermis and lung, Drosophila whole-embryo, adult Hydra, dendritic cells, the intestinal epithelium, and cells undergoing induced pluripotent stem cells (iPSC) reprogramming. We show how these results can be used to interpret the gradual dynamics of lineage structure along iPSC reprogramming. Together, we provide a framework that can be used to identify signatures of specific biological processes in single-cell data without prior knowledge and identify candidate genes associated with these processes. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Cellular lineage; Random matrix theory; Single-cell data; Spectral analysis
语种	英语
scopus关键词	adult; amino acid sequence; article; covariance; dendritic cell; Drosophila; embryo; human cell; Hydra; induced pluripotent stem cell; intestine epithelium; lung; mammal; nonhuman; nuclear reprogramming; phylogeny; single cell RNA seq; spectroscopy; United States
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180263
作者单位	School of Computer Science and Engineering, Racah Institute of Physics, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel; School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States
推荐引用方式 GB/T 7714	Nitzan M.,Brenner M.P.. Revealing lineage-related signals in single-cell gene expression using random matrix theory[J],2021,118(11).
APA	Nitzan M.,&Brenner M.P..(2021).Revealing lineage-related signals in single-cell gene expression using random matrix theory.Proceedings of the National Academy of Sciences of the United States of America,118(11).
MLA	Nitzan M.,et al."Revealing lineage-related signals in single-cell gene expression using random matrix theory".Proceedings of the National Academy of Sciences of the United States of America 118.11(2021).