MIGC: Pseudotime differentially expressed genes detection with mutual information

Trajectory analysis of single-cell RNA sequencing (scRNA-seq) data provides insights into cell differentiation and the underlying mechanisms at the gene level. Although various pseudotime inference methods have been proposed, the detection of pseudotime differentially expressed genes (DEGs) mainly relies on generalized additive model fitting followed by statistical hypothesis testing. However, considering that pseudotime inference is a prerequisite for pseudotime DEG detection, it would be beneficial to unify them into an integrated framework. Inspired by the concepts of information theory, we propose a trajectory analysis strategy called MIGC by capturing the mutual information between genes and cells, which can not only accurately infer cell pseudotime but also identify pseudotime DEGs. Experiments on multiple scRNA-seq datasets demonstrate that MIGC offers an effective trajectory analysis pipeline that outperforms baseline approaches in detecting pseudotime DEGs.