DTFLOW: Inference and Visualization of Single-cell Pseudo-temporal Trajectories 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 accurately. In this work we devise a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. This method consists of two major steps: namely a new dimension reduction method (i.e. Bhattacharyya kernel feature decomposition (BKFD)) and a novel approach, named Reverse Searching on kNN Graph (RSKG), to identify the underlying multi-branching processes of cellular differentiations. 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 pseudo-times 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 the new method with two state-of-the-art methods. Simulation results suggest that our proposed method has superior accuracy and strong robustness properties for constructing pseudo-time trajectories. Availability: DTFLOW is implemented in Python and available at https://github.com/statway/DTFLOW.