title: Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach.
abstract: Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.
authors.affiliation: Boston University, Boston, MA 02215.
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Pham
authors.affiliation: Boston University, Boston, MA 02215.
authors.forename: Luis
authors.initials: L
authors.lastname: Carvalho
authors.affiliation: Boston University, Boston, MA 02215.
authors.forename: Scott
authors.initials: S
authors.lastname: Schaus
authors.affiliation: Boston University, Boston, MA 02215.
authors.forename: Eric D
authors.initials: ED
authors.lastname: Kolaczyk
keywords: Bayesian Factor Models; Conditional Autoregressive Models; Confirmatory Factor Analysis; Drug Target Prediction; MCMC; Microarray; Network Biology