RT Journal Article
SR Electronic
T1 Inference of multiple trajectories in single cell RNA-seq data from RNA velocity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.09.30.321125
DO 10.1101/2020.09.30.321125
A1 Ziqi Zhang
A1 Xiuwei Zhang
YR 2020
UL http://biorxiv.org/content/early/2020/10/02/2020.09.30.321125.abstract
AB Trajectory inference methods are used to infer the developmental dynamics of a continuous biological process such as stem cell differentiation and cancer cell development. Most of the current trajectory inference methods infer cell developmental trajectories based on the transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like trees or cycles, and the directions of the trajectory can only be partly inferred when the root cell is provided. We present CellPaths, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPaths is able to find multiple high-resolution trajectories instead of one single trajectory from traditional trajectory inference methods, and the trajectory structure is no longer constrained to be of any specific topology. The direction information provided by RNA-velocity also allows CellPaths to automatically detect root cell and differentiation direction. We evaluate CellPaths on both real and synthetic datasets. The result shows that CellPaths finds more accurate and detailed trajectories compared to current state-of-the-art trajectory inference methods.Competing Interest StatementThe authors have declared no competing interest.