Abstract #301094

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JSM 2002 Abstract #301094
Activity Number: 283
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #301094
Title: Bayesian Differential Analysis of Gene Expression Data
Author(s): Paola Sebastiani*+
Affiliation(s): University of Massachusetts
Address: LGRT, Amherst, Massachusetts, 01003,
Keywords: genome, differential analysis
Abstract:

One challenge of the post-genome era is to understand the functions of genes and their interplay with proteins to create complex living systems. One avenue of research tries to identify the genes that are turned on/off in cells by measuring the expression level of thousands of genes with microarray technology. This work presents a novel Bayesian method to identify the genes that are differentially expressed across two conditions. The novelty of the method is the adoption of a new "contamination model" that accounts for the different components of variability affecting gene expression data measured with oligonucleotide arrays, and a new Bayesian goodness of fit measure to identify spiked genes. The accuracy of the method will be shown on real data.

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