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

## TL;DR

This paper introduces a new method called KDCA to better detect gene expression changes linked to various disease risk factors.

## Contribution

KDCA is a novel framework for differential co-expression analysis that works with all types of risk factors and reduces bias.

## Key findings

- KDCA controls type I error and increases power over existing methods in simulations.
- Applied to thyroid cancer data, KDCA found pathways related to age and BRAF mutations missed by other methods.

## Abstract

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.

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 approach. We then applied KDCA to The Cancer Genome Atlas thyroid data set and found several differentially co-expressed pathways by age of diagnosis and BRAF mutation status that were undetected by the eigengene method. Collectively, our results demonstrate that KDCA is a powerful testing framework that expands DCA applications in expression studies.

KDCA is publicly available in the R package kdca. The package can be downloaded at https://github.com/ajbass/kdca.