Welcome to the companion website for the book


edited by Giovanni Parmigiani, Elizabeth S Garrett, Rafael A Irizarry, Scott L Zeger,

from the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University
and the Department of Biostatistics, Bloomberg School of Public Health, at Johns Hopkins University

Published in March 2003 by Springer, NY

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 packages presented.

All software presented in the book 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.
Chapter Title  Authors  Software  Related Reference(s) 
Introduction Garrett, Irizarry, Parmigiani, and Zeger
Gene Filtering and Annotation Gentleman and Carey http://www.bioconductor.org
R packages for the analysis of cDNA microarray data Dudoit and Yang  http://www.bioconductor.org Dudoit et al. (2002) JASA 
Dudoit et al. (2001) Statistica Sinica 
Yang et al. (2001) Proceedings of SPIE
Dudoit et al. (2002) In preparation
An R Package for Oligonucleotide Array Statistical Analysis  Irizarry, Gautier and Cope affy Irizarry et al. (2002) Submitted
DNA-Chip Analyzer (d-Chip) Li and Wong DCHIP Li and Wong (2001) PNAS
Li and Wong (2001) Genomebiology
Expression Profiler  Vilo, Kapushesky, Kemmeren, Sarkans and Brazma Expression Profiler http://industry.ebi.ac.uk/~vilo/Publications/
An S-plus library for the Analysis of Microarray Data  Lee and O'Connell  S-PLUS 6 GENOMICS LIBRARY
DRAGON and DRAGON View: Methods for the Annotation, Analysis and Visualization of Large-Scale Gene Expression Data Bouton, Henry, Colantuoni and Pevsner DRAGON Bouton and Pevsner (2000) Bioinformatics
SNOMAD: User-Friendly Web Tools for the Standardization and NOrmalization of MicroArray Data Colantuoni, Henry, Zeger and Pevsner SNOMAD Colantuoni et al. (2002) Submitted
Microarray analysis using the MicroArray Explorer  Lemkin, Thornwall and Evans  MAExplorer 
Parametric Empirical Bayes Methods for Micorarrays Newton and Kendziorski  EBarrays Kendziorski, C.M., M.A. Newton, H. Lan, and M.N. Gould (2003)
SAM thresholding and false discovery rates for detecting differential gene expression in DNA Microarrays Storey and Tibshirani  SAM Storey (2002) JRSS-B 
Tusher, Tibshirani and Chu (2001) PNAS
Adaptive Gene Picking with  Microarray Data: Detecting Important Low Abundance Signals Lin, Nadler, Lan, Attie and Yandell  Pickgene Tech report
MAANOVA: a software package for the analysis of spotted cDNA microarray experiments  Wu, Kerr, Cui and Churchill MAANOVA
GeneClust Do, Broom and Wen GeneClust
POE: Statistical Methods for Qualitative Analysis Of Expression Garrett and Parmigiani  POE A statistical framework for molecular classification in cancer
Bayesian Decomposition Ochs  Bayesian Decomposition Ochs et al. (1999) J. of Magnetic Resonance
Ochs et al. (2001) AIP Conf. Proceedings
Ochs et al. (2001) Magnetic Resonance in Medicine
Moloshok et. al. (2001) Bioinformatics
Cluster Analysis of Gene Expression Dynamics  Sebastiani, Ramoni and Kohane CAGED M. Ramoni, P. Sebastiani and I.S. Kohane.
Cluster Analysis of Gene Expression Dynamics. Proc Nat Acad Sci USA. 99(14):9121-6. 2002.
Relevance Networks Butte and Kohane RelNet Butte and Kohane
Unsupervised Knowledge Discovery in Medical Databases Using Relevance Networks 

These are additional links referenced in the book or closely related to some of the Chapters' work:

Microarray Expression Data Analysis References

Microarray & Data Analysis

Microarrays databases on the WWW

Microarrays Software Comparison

SMAWEHI: An R Library for Statistical Microarray Analysis

BRB ArrayTools

Microarray tools from TIGR

Cluster and TreeView



OOMAL: Object Oriented Microarray Library in S-PLUS

GeneExpression Omnibus