A factor model to analyze heterogeneity in gene expression

Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene dependence structure. This leads to correlation among test statistics which affects a strong control of the false discovery proportion. A recent method called FAMT allows capturing the gene dependence into factors in order to improve high-dimensional multiple testing procedures. In the subsequent analyses aiming at a functional characterization of the differentially expressed genes, our study shows how these factors can be used both to identify the components of expression heterogeneity and to give more insight into the underlying biological processes. Results: The use of factors to characterize simple patterns of heterogeneity is first demonstrated on illustrative gene expression data sets. An expression data set primarily generated to map QTL for fatness in chickens is then analyzed. Contrarily to the analys...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigue, David Causeur
• Keywords: BMCBI 2010, Gene Dependence, Gene Expressions, Transcriptomic Data