Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network

• Activity Number: 77
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Lisa Pham*+ and Luis Carvalho and Scott Schaus and Eric D Kolaczyk
• Companies: Boston University and Boston University and Boston University and Boston University
• Keywords: bayesian ; network ; gene expression ; microarray ; factor analysis

Abstract

Proteins interact with each other to form biological pathways that perform numerous tasks in the cell. In turn, functional relationships exist among these pathways, creating a larger network that works to maintain cell homeostasis. A major challenge in many disease and drug studies is in identifying the specific factors of a biological network that play key roles in determining the cell's fate.

Our goal is the identification of perturbed pathways from gene expression data, which we approach as a task in statistical modeling and inference. We develop a two-level statistical model, where (i) the first level is a confirmatory latent factor model that captures the relationship between gene expression and biological pathways, and (ii) the second level is a simultaneous equation model that models the behavior within an underlying network of pathways induced by an unknown perturbation.

Detection of a perturbed pathway(s) is accomplished through statistical inference on latent variables representing perturbation targets, using principles of classical Bayesian analysis. We illustrate using simulation studies as well as gene transcription cancer profiles from The Cancer Genome Atlas.