iFad: an integrative factor analysis model for drug-pathway association inference.

MOTIVATION: Pathway-based drug discovery considers the therapeutic effects of compounds in the global physiological environment. This approach has been gaining popularity in recent years because the target pathways and mechanism of action for many compounds are still unknown, and there are also some unexpected off-target effects. Therefore, the inference of drug-pathway associations is a crucial step to fully realize the potential of system-based pharmacological research. Transcriptome data offer valuable information on drug-pathway targets because the pathway activities may be reflected through gene expression levels. Hence, it is of great interest to jointly analyze the drug sensitivity and gene expression data from the same set of samples to investigate the gene-pathway-drug-pathway associations.
RESULTS: We have developed iFad, a Bayesian sparse factor analysis model to jointly analyze the paired gene expression and drug sensitivity datasets measured across the same panel of samples. The model enables direct incorporation of prior knowledge regarding gene-pathway and/or drug-pathway associations to aid the discovery of new association relationships. We use a collapsed Gibbs sampling algorithm for inference. Satisfactory performance of the proposed model was found for both simulated datasets and real data collected on the NCI-60 cell lines. Our results suggest that iFad is a promising approach for the identification of drug targets. This model also provides a general statistical framework for pathway-based integrative analysis of other types of -omics data. AVAILABILITY: The R package 'iFad' and real NCI-60 dataset used are available at http://bioinformatics.med.yale.edu/group.