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Title: Bayesian analysis for multi-subject time course gene expression with an application to vaccine immune response Authors:  Allison Tegge - Virginia Tech (United States) [presenting]
Marco Ferreira - Virginia Tech (United States)
Abstract: A Bayesian methodology is introduced for the analysis of multi-subject time-course gene expression data. Our methodology facilitates the study of transcriptional changes through time. Specifically, we develop a fully Bayesian approach to detect differentially expressed genes that reduces the high dimensionality of time-course data by empirical orthogonal functions. The proposed model assumes distinct temporal patterns for differentially and non-differentially expressed genes, and borrows strength across genes and subjects to increase detection power. We illustrate the usefulness and flexibility of our methodology with an analysis of an RNA-seq data set from B cells to study their temporal response pattern to the human influenza vaccine.