Publication Date

11-22-2023

Journal

Briefings in Bioinformatics

DOI

10.1093/bib/bbad380

PMID

38113079

PMCID

PMC10729864

PubMedCentral® Posted Date

12-18-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, RNA-Seq, Gene Regulatory Networks, Models, Statistical, Sequence Analysis, RNA, Gene Expression Profiling, gene network, co-regulation network, beta-binomial statistical model, non-linear correlation, Simpson’s paradox

Abstract

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12 000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpson's paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

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