MIGC: Pseudotime differentially expressed genes detection with mutual information.
- 1State Key Laboratory of Biopharmaceutical Preparation and Delivery Institute of Process Engineering Chinese Academy of Sciences Beijing China.
Abstract
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.
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BibTeX
@article{li2026migc,
title = {{MIGC: Pseudotime differentially expressed genes detection with mutual information.}},
author = {Jiachen Li and Ye Yuan},
journal = {Quantitative biology (Beijing, China)},
year = {2026},
month = oct,
doi = {10.1002/qub2.70057},
url = {https://europepmc.org/article/MED/42903567},
note = {CC BY},
}
RIS
TY - JOUR
TI - MIGC: Pseudotime differentially expressed genes detection with mutual information.
AU - Jiachen Li
AU - Ye Yuan
PY - 2026
DA - 2026/10/19
JO - Quantitative biology (Beijing, China)
DO - 10.1002/qub2.70057
UR - https://europepmc.org/article/MED/42903567
AB - 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.
SN - 2095-4689
SN - 2095-4697
ER -