DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data

doi:10.1093/bioinformatics/bty1009

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	DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data
	Chen, Ziwei 1,2; An, Shaokun 1,2; Bai, Xiangqi 1,2; Gong, Fuzhou 1,2; Ma, Liang 2,3; Wan, Lin1,2
刊名	BIOINFORMATICS
	2019-08-01
卷号	35 期号:15 页码:2593-2601
ISSN号	1367-4803
DOI	10.1093/bioinformatics/bty1009
英文摘要	Motivation Visualizing and reconstructing cell developmental trajectories intrinsically embedded in high-dimensional expression profiles of single-cell RNA sequencing (scRNA-seq) snapshot data are computationally intriguing, but challenging. Results We propose DensityPath, an algorithm allowing (i) visualization of the intrinsic structure of scRNA-seq data on an embedded 2-d space and (ii) reconstruction of an optimal cell state-transition path on the density landscape. DensityPath powerfully handles high dimensionality and heterogeneity of scRNA-seq data by (i) revealing the intrinsic structures of data, while adopting a non-linear dimension reduction algorithm, termed elastic embedding, which can preserve both local and global structures of the data; and (ii) extracting the topological features of high-density, level-set clusters from a single-cell multimodal density landscape of transcriptional heterogeneity, as the representative cell states. DensityPath reconstructs the optimal cell state-transition path by finding the geodesic minimum spanning tree of representative cell states on the density landscape, establishing a least action path with the minimum-transition-energy of cell fate decisions. We demonstrate that DensityPath can ably reconstruct complex trajectories of cell development, e.g. those with multiple bifurcating and trifurcating branches, while maintaining computational efficiency. Moreover, DensityPath has high accuracy for pseudotime calculation and branch assignment on real scRNA-seq, as well as simulated datasets. DensityPath is robust to parameter choices, as well as permutations of data. Availability and implementation DensityPath software is available at https://github.com/ucasdp/DensityPath. Supplementary information Supplementary data are available at Bioinformatics online.
资助项目	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 Chinese Academy of Sciences[XDB13050000] ; National Center for Mathematics and Interdisciplinary Sciences of Chinese Academy of Sciences ; LSC of Chinese Academy of Sciences ; Youth Innovation Promotion Association of Chinese Academy of Sciences ; Mathematical Biosciences Institute (MBI) at Ohio State University ; National Science Foundation[DMS 1440386]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:000484378200009
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/35572]
专题	系统科学研究所
通讯作者	Ma, Liang; Wan, Lin
作者单位	1.Chinese Acad Sci, NCMIS, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Beijing Inst Genom, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Chen, Ziwei,An, Shaokun,Bai, Xiangqi,et al. DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data[J]. BIOINFORMATICS,2019,35(15):2593-2601.
APA	Chen, Ziwei,An, Shaokun,Bai, Xiangqi,Gong, Fuzhou,Ma, Liang,&Wan, Lin.(2019).DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data.BIOINFORMATICS,35(15),2593-2601.
MLA	Chen, Ziwei,et al."DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data".BIOINFORMATICS 35.15(2019):2593-2601.