DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurat...

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Autores principales:	Jiangyong Wei, Tianshou Zhou, Xinan Zhang, Tianhai Tian
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Single-cell heterogeneity Pseudotime trajectory Manifold learning Bhattacharyya kernel Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/6ba432b316d04ca8b6b67af3b350dcb3
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spelling	oai:doaj.org-article:6ba432b316d04ca8b6b67af3b350dcb32021-11-16T04:09:23ZDTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation1672-022910.1016/j.gpb.2020.08.003https://doaj.org/article/6ba432b316d04ca8b6b67af3b350dcb32021-04-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1672022921000474https://doaj.org/toc/1672-0229One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.Jiangyong WeiTianshou ZhouXinan ZhangTianhai TianElsevierarticleSingle-cell heterogeneityPseudotime trajectoryManifold learningBhattacharyya kernelBiology (General)QH301-705.5ENGenomics, Proteomics & Bioinformatics, Vol 19, Iss 2, Pp 306-318 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Single-cell heterogeneity Pseudotime trajectory Manifold learning Bhattacharyya kernel Biology (General) QH301-705.5
spellingShingle	Single-cell heterogeneity Pseudotime trajectory Manifold learning Bhattacharyya kernel Biology (General) QH301-705.5 Jiangyong Wei Tianshou Zhou Xinan Zhang Tianhai Tian DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
description	One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.
format	article
author	Jiangyong Wei Tianshou Zhou Xinan Zhang Tianhai Tian
author_facet	Jiangyong Wei Tianshou Zhou Xinan Zhang Tianhai Tian
author_sort	Jiangyong Wei
title	DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
title_short	DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
title_full	DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
title_fullStr	DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
title_full_unstemmed	DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
title_sort	dtflow: inference and visualization of single-cell pseudotime trajectory using diffusion propagation
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6ba432b316d04ca8b6b67af3b350dcb3
work_keys_str_mv	AT jiangyongwei dtflowinferenceandvisualizationofsinglecellpseudotimetrajectoryusingdiffusionpropagation AT tianshouzhou dtflowinferenceandvisualizationofsinglecellpseudotimetrajectoryusingdiffusionpropagation AT xinanzhang dtflowinferenceandvisualizationofsinglecellpseudotimetrajectoryusingdiffusionpropagation AT tianhaitian dtflowinferenceandvisualizationofsinglecellpseudotimetrajectoryusingdiffusionpropagation
_version_	1718426728988147712

DTFLOW: Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

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