Language
English
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
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.
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
Published Open-Access
yes
Recommended Citation
Wang, Jiasheng; Wan, Ying-Wooi; Al-Ouran, Rami; et al., "CoRegNet: Unraveling Gene Co-Regulation Networks from Public RNA-Seq Repositories Using a Beta-Binomial Statistical Model" (2023). Faculty and Staff Publications. 3438.
https://digitalcommons.library.tmc.edu/baylor_docs/3438