Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection

Noh, Heeju; Shoemaker, Jason E; Gunawan, Rudiyanto

We applied ProTINA to three datasets of drug treatments from NCI-DREAM drug synergy challenge (24) , genotoxicity study (25) and chromosome drug targeting study (26) , and to gene expression data of human lung cancer cell Calu-3 from influenza A viral infection studies (27) (28) (29) (30) . For NCI-DREAM drug synergy challenge, we obtained the raw Affymetrix Human Genome U219 microarray data from Gene Expression Omnibus (GEO) database (31) (accession number: GSE51068). The raw data were first nor-PAGE 3 OF 11 Nucleic Acids Research, 2018, Vol. 46, No. 6 e34 malized and transformed into log 2 -scaled expressions using justRMA function in the affy package of Bioconductor (32) . Then, the log 2 fold change (log 2 FC) differential expressions and their statistical significance (Benjamini-Hochberg adjusted P-values) were calculated using a linear fit model and empirical Bayes method in the limma package of Bionconductor. Three samples from the drug treatment using the low dose of Aclacinomycin A were dropped because all of the log 2 FC expressions were close to 1 and thus not statistically significant. The probe sets were mapped to gene symbols using hgu219.db annotation package (Entrez Gene database as of 27 September 2015). In the case of multiple probe sets mapping to a gene symbol, we assigned the log 2 FC from the probe set with the smallest average adjusted P-value over the samples.