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

• Published in: Genomics, proteomics & bioinformatics proteomics & bioinformatics, 2021-04, Vol.19 (2), p.306-318
• Main Authors: Wei, Jiangyong, Zhou, Tianshou, Zhang, Xinan, Tian, Tianhai
• Format: Article
• Language: English
• Subjects: Bhattacharyya kernel; Manifold learning; Method; Pseudotime trajectory; Single-cell heterogeneity
• ISSN: 1672-0229; 2210-3244
• DOI: 10.1016/j.gpb.2020.08.003

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.