| Title: | Co-expression network multivariate regression |
| Journal: | Briefings in Bioinformatics |
| Published: | 1 Mar 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41870131/ |
| DOI: | https://doi.org/10.1093/bib/bbag125 |
Publication 18245
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Abstract
Accounting for dependence among high-dimensional variables in omics data analysis is critical to obtain accurate and reliable statistical inference. Although latent, omics variables often exhibit structured correlation/co-expression patterns. However, there are few methods explicitly accounting for such structured dependence in the statistical analysis of omics data (e.g. differential expression analysis). To address this methodological gap, we propose a Coexpression network multivariate Regression (CoReg), which integrates co-expression network structure into multivariate regression analysis to precisely account for the inter-correlations (dependence) among omics variables. We show in simulations that CoReg substantially improves the accuracy of statistical inference and replicability across studies. These findings suggest that CoReg provides an alternative approach for omics data association analysis with dependence adjustment, analogous to the role of mixed-effects models in handling repeated measures in lower-dimensional settings.</p>
7 Keywords
- Computational Biology
- Computer Simulation
- Gene Expression Profiling
- Gene Regulatory Networks
- Humans
- Multivariate Analysis
- Regression Analysis
3 Authors
- Hwiyoung Lee
- Yezhi Pan
- Shuo Chen
1 Application
| Application ID | Title |
|---|---|
| 74376 | Redefining Trans-Neuropsychiatric Disorder Patterns through Big Data and Machine Learning |
Enabling scientific discoveries that improve human health