Algorithm improves detection of differentially expressed genes in large single-cell trajectory data sets

Single-cell RNA sequencing (scRNA-seq) is a method for measuring gene expression in individual cells, allowing observation of various cellular processes, including cell differentiation, the cell cycle and stimulus response, for each unique cell instead of averaging across millions of cells. It provides high-resolution snapshots of biological processes. However, it does not track the same cell continuously over time. To address this limitation, trajectory inference approaches have been developed that computationally arrange cellular snapshots along an inferred developmental trajectory known as pseudotime.