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Cellular response to a perturbation is the result of a dynamic system ofbiological variables linked in a complex network. A major challenge in drug anddisease studies is identifying the key factors of a biological network that areessential in determining the cell's fate.  Here our goal is the identification of perturbed pathways fromhigh-throughput gene expression data. We develop a three-level hierarchicalmodel, where (i) the first level captures the relationship between geneexpression and biological pathways using confirmatory factor analysis, (ii) thesecond level models the behavior within an underlying network of pathwaysinduced by an unknown perturbation using a conditional autoregressive model,and (iii) the third level is a spike-and-slab prior on the perturbations. Wethen identify perturbations through posterior-based variable selection.  We illustrate our approach using gene transcription drug perturbationprofiles from the DREAM7 drug sensitivity predication challenge data set. Ourproposed method identified regulatory pathways that are known to play acausative role and that were not readily resolved using gene set enrichmentanalysis or exploratory factor models. Simulation results are presentedassessing the performance of this model relative to a network-free variant andits robustness to inaccuracies in biological databases.

Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach