Identifying disease similarity

Cyber-T's (Kayala and Baldi, 2012) regularized t-test performed on z-transformed expression values was used as a measure of a gene's differential activity. The associated T-statistic provided insight into the difference in mean expression of a gene across conditions and was referred to as the differential gene activity (DGA) score for each disease state (Table S0). Partial pearson correlation of DGA scores across diseases, quantified disease similarity based on expression profiles. Use of partial correlation have been shown to be effective in factoring out dependencies such as variation in tissue types and experimental conditions (Suthram et al., 2010). The “pcor” function available through the “ppcor” package in R (Kim, 2015) was utilized to calculate all possible pairwise partial correlations between each pair of samples (here diseases) while eliminating the effect of all other samples. We identified disease clusters using hierarchical (complete-linkage) clustering of partial correlation.