Abstract
We will describe a new algorithm for finding coherent and flexible modules in 3-way data, e.g., measurements of gene expression for a group of patients over a sequence of time points. Our method can identify both core modules that appear in multiple patients and patient-specific augmentations of these core modules that contain additional genes. Our algorithm uses a hierarchical Bayesian data model and Gibbs sampling. We demonstrate its utility and advantage in analysis of gene expression time series following septic shock response, and in analyzing brain fMRI time series of subjects at rest. Networks are used to put the results in biological and medical context.
Joint work with D. Amar, A. Maron-Katz, D. Yekutieli (Tel Aviv University), and T. Hendler (Sourasky Medical Center)