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.
单细胞数据分析中的一个主要挑战是如何确定细胞的发育轨迹。尽管近年来已经开展了大量研究，但仍需要更为有效的方法来准确地推断细胞的发育过程。为解决这个问题，我们设计了一种新的算法DTFLOW来确定具有多分支的单细胞数据伪时间轨迹。该算法包含两个主要步骤。首先构建了一个全新的基于巴氏核特征分解降维算法BKFD，在该降维算法中我们基于重启随机游走算法将每个细胞表示为一个平稳分布，并利用该分布刻画细胞发育状态的转变。该算法的核心是利用巴氏核矩阵来定义一种新的距离度量并用于计算单细胞的伪时间。第二个主要步骤是利用在第一步得到的伪时间在K近邻图上逆向搜索，得到了一种新的多分支识别算法RSKG。我们利用四个单细胞数据集对DTFLOW算法进行严格的验证并与二个最新发表的算法进行了比较。研究结果表明，与常规伪时间轨迹推断算法相比，DTFLOW具有较高的精度和较强的稳健性。





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