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Microarray is an important technology which enables people to investigate the expression levels of thousands of genes at the same time. A series of methods are proposed in this book to to detect differentially expressed genes while controlling the false discovery rate. In Chapter 1 and 2, a brief introduction of the Affymetrix GeneChip microarray technology and a literature review of the related works on this matter is provided.In Chapter 3, a t-mixture model based method is proposed to detect differentially expressed genes. In Chapter 4, a t-mixture model based false discovery rate estimator is proposed to overcome several problems of the current empirical false discovery rate estimators. In Chapter 5, a two-step false discovery rate estimation procedure is proposed to correct the overestimation of the false discovery rate caused by differentially expressed genes. In Chapter 6, a novel estimator is developed to estimate the proportion of equivalently expressed genes, which is an important component of the false discovery rate estimators.

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Shuo Jiao obtained his PhD in Statistics at University of Nebraska Lincoln. He is currently a staff scientist at Fred Hutchinson Cancer Research Center. He has several publications in the gene expression profiling and false discovery rate estimation in top Bioinformatics journal. His research interest also includes the analysis the GWAS data.

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