Minimum Redundancy Maximal Relevance Gene Selection of Apoptosis Pathway Genes in Peripheral Blood Mononuclear Cells of HIV-infected Patients with Antiretroviral Therapy-associated Mitochondrial Toxicity

Background: We previously identified differentially expressed genes on the basis of false discovery rate adjusted P value using empirical Bayes moderated tests. However, that approach yielded a subset of differentially expressed genes without accounting for redundancy between the selected genes. Methods: This study is a secondary analysis of a case-control study of the effect of antiretroviral therapy (ART) on apoptosis pathway genes comprising of 16 cases (HIV infected with mitochondrial toxicity) and 16 controls (uninfected). We applied the maximum relevance minimum redundancy (mRMR) algorithm on the genes that were differentially expressed between the cases and controls. The mRMR algorithm iteratively selects features (genes) that are maximally relevant for class prediction and minimally redundant. We implemented m-fold cross validation using machine learning classifiers and tested the prediction accuracy of the two mRMR genes. We next used network analysis to estimate the association between the differentially expressed genes using partial correlations, relative importance estimates, and centrality measures of each item. The Spinglass algorithm was used to identify clusters of gene communities.Results: The mRMR algorithm ranked DFFA and TNFRSF1A, two of the upregulated proapoptotic genes, on the top. The overall prediction accuracy was 90%, which clearly show that the mRMR gene sets outperforms the performance of the gene sets based on gene expression analyses. FASLG had highest centrality in cases with ABL1 in controls.Conclusion: The mRMR algorithm and network analysis revealed a new correlation of genes associated with mitochondrial toxicity.