The Analysis of Gene Expression Data

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