A Pseudo-Value Regression Approach for Differential Network Analysis of Co-Expression Data

TL;DR

This paper introduces PRANA, a novel pseudo-value regression method for differential network analysis that adjusts for clinical covariates, outperforming existing methods in identifying differentially connected genes in gene expression data.

Contribution

The paper presents the first regression-based approach for differential network analysis incorporating clinical covariates, improving accuracy over existing methods.

Findings

PRANA outperforms dnapath and DINGO in simulation studies.

Adjusting for covariates enhances differential network analysis accuracy.

PRANA successfully identified COPD-related genes in real data.

Abstract

The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a Pseudo-value Regression Approach for Network Analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information (MI) criteria, followed by pseudo-value calculations, which are then entered into a robust regression model. This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus (GEO) database to identify DC genes that are associated with chronic obstructive pulmonary disease (COPD) to demonstrate its utility. To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.