Chapter 12 - Analyzing Gene Expression Data

Abstract

In recent years, microarray technology has enabled researchers to measure the expression level of all genes in an organism, thereby providing the data to investigate the causal relationships among the genes. Early tools for analyzing microarray data used clustering algorithms. These algorithms determine groups of genes that have similar expression levels in a given experiment. Modeling gene interaction using a Bayesian network aids in understanding the causal pattern. By analyzing gene expression data, this chapter deals with the dependence and causal relationships between the expression levels of certain genes and concerns model averaging. It also reviews how microarray technology enables the simultaneous measurement of the expression levels of different genes and then discusses a bootstrap approach to analyzing gene expression data. Furthermore, a module network method is presented for analyzing gene expression data.