A powerful framework for differential co-expression analysis of general risk factors.

ABSTRACT:

Motivation

Differential co-expression analysis (DCA) aims to identify genes in a pathway whose shared expression depends on a risk factor. While DCA provides insights into the biological activity of diseases, existing methods are limited to categorical risk factors and/or suffer from bias due to batch and variance-specific effects. We propose a new framework, Kernel-based DCA (KDCA), that harnesses correlation patterns between genes in a pathway to detect differential co-expression arising from general (i.e. continuous, discrete, or categorical) risk factors.

Results

Using various simulated pathway architectures, we find that KDCA accounts for common sources of bias to control the type I error rate while substantially increasing the power compared to the standard eigengene a

SUBMITTER: Bass AJ 

PROVIDER: S-EPMC12619644 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature