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.
Indice testuale
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.
