Fold-change estimation of differentially expressed genes using mixture mixed-model

Statistical Applications in Genetics and Molecular Biology
Arief GusnantoYudi Pawitan

Abstract

Microarray experiments produce expression measurements for thousands of genes simultaneously, though usually for a small number of RNA samples. The most common problem is the identification of genes that are differentially expressed between different groups of samples or biological conditions. As the number of genes far exceeds the number of RNA samples, the inherent multiplicity poses a severe problem in both hypothesis testing and effect estimation. While much of the recent literature is focused on the hypothesis aspects, we concentrate in this paper on effect estimation as a tool for the identification of differentially expressed genes. We propose a linear mixed model where the random effects are assumed to follow a mixture distribution, and study in detail the case of three normals, corresponding to genes that are down-, up- or non regulated. Our approach leads to a new type of non-linear shrinkage estimation, where a proportion of estimates is shrunk to zero, while the rest follows standard linear shrinkage. This allows us to estimate the log fold-change of the genes involved and to identify those that are differentially expressed within the same model framework. We investigate the operating characteristics of our method u...Continue Reading

Citations

Apr 24, 2008·International Journal of Plant Genomics·Zhenyu Jia, Shizhong Xu
Jan 15, 2010·FEMS Microbiology Letters·Xiangyang ShiDonald A Cooksey
Mar 14, 2007·Current Opinion in Lipidology·Arief GusnantoYudi Pawitan
Feb 17, 2009·Applied and Environmental Microbiology·Xiang Yang ShiDonald A Cooksey
Sep 11, 2007·Applied and Environmental Microbiology·Xiang Yang ShiDonald A Cooksey

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