Recent advances in trajectory inference from single-cell omics data

Trajectory inference methods have emerged as a novel class of single-cell bioinformatics tools to study cellular dynamics at unprecedented resolution. Initial development focused on adapting methods based on clustering or graph traversal, but recent advances extend the field in different directions. A first class of methods includes novel probabilistic methods that report uncertainties about their outputs, and new methods that consider complementary knowledge, such as unspliced mRNA, time point information, or other types of omics data to construct the trajectory. A second class of methods uses the obtained trajectories as a starting point for novel analyses, such as visualization approaches, new types of statistical analyses, and the possibility to render static analyses more dynamic, such as dynamic gene regulatory network inference.