Synopses & Reviews
This book presents practical approaches for the analysis of data from gene expression microarrays. Each chapter describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. Methods cover all aspects of statistical analysis of microarrays, from annotation and filtering to clustering and classification. Chapters are written by the developers of the software. All software packages described are free to academic users. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. 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 analysts without formal quantitative training. Most chapters are directed at microarray data analysts with master-level training in computer science, biostatistics or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers. The team of editors is from the Johns Hopkins Schools of Medicine and Public Health and has been involved with developing methods and software for microarray data analysis since the inception of this technology. Giovanni Parmigiani is Associate Professor of Oncology, Pathology and Biostatistics. He is the author of the book on "Modeling in Medical decision Making," a fellow of the ASA, and a recipient of the Savage Awards for Bayesian statistics. Elizabeth S. Garrett is Assistant Professor of Oncology and Biostatistics, and recipient of the Abbey Award for statistical education. Rafael A Irizarry is Assistant Professor of Biostatistics, and recipient of the Noether Award for non-parametric statistics. Scott L. Zeger is Professor and chair of Biostatistics. He is co-author of the book "Longitudinal Data Analysis," a fellow of the ASA and recipient of the Spiegelman Award for public health statistics.
This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences.
Table of Contents
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