  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.
