DTFLOW:Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation
- Author:
Wei JIANGYONG
1
,
2
;
Zhou TIANSHOU
;
Zhang XINAN
;
Tian TIANHAI
Author Information
1. College of Science,Huazhong Agricultural University,Wuhan 430070,China
2. School of Statistics and Mathematics,Zhongnan University of Economics and Law,Wuhan 430073,China
- Keywords:
Single-cell heterogeneity;
Pseudotime trajectory;
Manifold learning;
Bhattacharyya kernel
- From:
Genomics, Proteomics & Bioinformatics
2021;19(2):306-318
- CountryChina
- Language:Chinese
-
Abstract:
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 dis-tribution for each cell to represent the transition of cellular developmental states based on the ran-dom 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.
