Methods of Microarray Data Analysis III

Papers from CAMDA’ 02

  • Kimberly F. Johnson
  • Simon M. Lin

Table of contents

  1. Front Matter
    Pages i-xi
  2. Pages 1-1
  3. Tutorials

    1. Michael F. Ochs, Erica A. Golemis
      Pages 9-24
    2. Kevin R. Coombes, Jing Wang, Lynne V. Abruzzo
      Pages 25-40
    3. Ronald K. Pearson, Gregory E. Gonye, James S. Schwaber
      Pages 41-55
  4. Best Presentation Award

    1. David N. Stivers, Jing Wang, Gary L. Rosner, Kevin R. Coombes
      Pages 59-72
  5. Analyzing Images

  6. Normalizing Raw Data

    1. Liling Warren, Ben Liu
      Pages 105-121
  7. Characterizing Technical and Biological Variance

    1. Madhuchhanda Bhattacharjee, Colin Pritchard, Mikko J. Sillanpää, Elja Arjas
      Pages 155-172
  8. Investigating Cross Hybridization on Oligonucleotide Microarrays

    1. Li Zhang, Kevin R. Coombes, Lianchun Xiao
      Pages 175-184
    2. Seman Kachalo, Zarema Arbieva, Jie Liang
      Pages 185-198
    3. Wen-Ping Hsieh, Tzu-Ming Chu, Russ Wolfinger
      Pages 199-208
  9. Finding Patterns and Seeking Biological Explanations

    1. T. D. Moloshok, D. Datta, A. V. Kossenkov, M. F. Ochs
      Pages 211-231
    2. Ramón Díaz-Uriarte, Fátima Al-Shahrour, Joaquín Dopazo
      Pages 233-247
  10. Back Matter
    Pages 249-252

About this book


As microarray technology has matured, data analysis methods have advanced as well. Methods Of Microarray Data Analysis III is the third book in this pioneering series dedicated to the existing new field of microarrays. While initial techniques focused on classification exercises (volume I of this series), and later on pattern extraction (volume II of this series), this volume focuses on data quality issues. Problems such as background noise determination, analysis of variance, and errors in data handling are highlighted.

Three tutorial papers are presented to assist with a basic understanding of underlying principles in microarray data analysis, and twelve new papers are highlighted analyzing the same CAMDA'02 datasets: the Project Normal data set or the Affymetrix Latin Square data set. A comparative study of these analytical methodologies brings to light problems, solutions and new ideas. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of art of microarray data analysis.


Microarray Termination classification gene expression genes hybridization modeling simulation

Editors and affiliations

  • Kimberly F. Johnson
    • 1
  • Simon M. Lin
    • 2
  1. 1.Cancer Center Information SystemsDuke University Medical CenterDurham
  2. 2.Duke Bioinformatics Shared ResourceDuke University Medical CenterDurham

Bibliographic information

  • DOI
  • Copyright Information Springer Science + Business Media, Inc. 2003
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-7582-7
  • Online ISBN 978-0-306-48354-7
  • Buy this book on publisher's site