ArticleQuantitative biology (Beijing, China)2026
MIGC: Pseudotime differentially expressed genes detection with mutual information.
Article in Quantitative biology (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.