Statistical Genetics for Genomic Data Analysis

Abstract

In this chapter, we briefly summarize the emerging statistical concepts and approaches that have been recently developed and applied to the analysis of genomic data such as microarray gene expression data. In the first section we introduce the general background and critical issues in statistical sciences for genomic data analysis. The second section describes a novel concept of statistical significance, the so-called false discovery rate, the rate of false positives among all positive findings, which has been suggested to control the error rate of numerous false positives in large screening biological data analysis. In the next section we introduce two recent statistical testing methods: significance analysis of microarray (SAM) significance analysis of microarray (SAM) and local pooled error (LPE) local pooled error (LPE) tests. The latter in particular, which is significantly strengthened by pooling error information from adjacent genes at local intensity ranges, is useful to analyze microarray data with limited replication. The fourth section introduces analysis of variation (ANOVA) and heterogenous error modeling (HEM) heterogeneous error model (HEM) approaches that have been suggested for analyzing microarray data obtained from multiple experimental and/or biological conditions. The last two sections describe data exploration and discovery tools largely termed supervised learning and unsupervised learning. The former approaches include several multivariate statistical methods for the investigation of coexpression patterns of multiple genes, and the latter approaches are used as classification methods to discover genetic markers for predicting important subclasses of human diseases. Most of the statistical software packages for the approaches introduced in this chapter are freely available at the open-source bioinformatics software web site (Bioconductor; http://www.bioconductor.org/).