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.
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.
