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Thedevelopmentoftechnologiesforhigh–throughputmeasurementofgene expression in biological system is providing powerful new tools for inv- tigating the transcriptome on a genomic scale, and across diverse biol- ical systems and experimental designs. This technological transformation is generating an increasing demand for data analysis in biological inv- tigations of gene expression. This book focuses on data analysis of gene expression microarrays. The goal is to provide guidance to practitioners in deciding which statistical approaches and packages may be indicated for their projects, in choosing among the various options provided by those packages, and in correctly interpreting the results. The book is a collection of chapters written by authors of statistical so- ware for microarray data analysis. Each chapter describes the conceptual and methodological underpinning of data analysis tools as well as their software implementation, and will enable readers to both understand and implement an analysis approach. Methods touch on all aspects of statis- cal analysis of microarrays, from annotation and ?ltering to clustering and classi?cation. All software packages described are free to academic users. The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion and are designed to be accessible to microarray data analystswithoutformalquantitativetraining.Mostchaptersaredirectedat microarray data analysts with master’s-level training in computer science, biostatistics, or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.
Introduction * Visualization and annotation of genomic experiments * Bioconductor R packages for exploratory data analysis and normalization of cDNA microarray data * An R package for analyses of affymetrix oligonucleotide arrays * DNA-Chip analyzer (d-Chip) * Expression Profiler * An S-Plus library for the analysis of microarray data * DRAGON and DRAGON View: Methods for the annotation, analysis, and visualization of large-scale gene expression data * SNOMAD: User-friendly web tools for the standardization and normalization of microarry data * Microarray analysis using the MicroArray Explorer * Parametric empirical Bayes methods for microarrays * SAM thresholding and false discovery rates for detecting differential gene expression in DNA microarrays * Adaptive gene picking with microarray data: Detecting important low abundance signals * MAANOVA: A software package for the analysis of spotted cDNA microarray experiments * GeneClust * POE Statistical Tools for molecular profiling * Bayesian decomposition * Cluster analysis of gene expression dynamics * Relevance networks: A first step towards finding genetic regulatory networks within microarray data