Methods of Microarray Data Analysis III: Papers from CAMDA’ 02

✍ Scribed by Kimberly F. Johnson, Simon M. Lin (eds.)

• Publisher: Springer US
• Year: 2004
• Tongue: English
• Leaves: 246
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Introduction....Pages 1-1
The Biology behind Gene Expression: A Basic Tutorial....Pages 9-24
Monitoring the Quality of Microarray Experiments....Pages 25-40
Outliers in Microarray Data Analysis....Pages 41-55
Organ-Specific Differences in Gene Expression and Unigene Annotations Describing Source Material....Pages 59-72
Characterization, Modeling, and Simulation of Mouse Microarray Data....Pages 75-91
Topological Adjustments to Genechip Expression Values....Pages 93-101
Comparison of Normalization Methods for cDNA Microarrays....Pages 105-121
Simultaneous Assessment of Transcriptomic Variability and Tissue Effects in the Normal Mouse....Pages 125-137
How Many Mice and How Many Arrays? Replication in Mouse cDNA Microarray Experiments....Pages 139-154
Bayesian Characterization of Natural Variation in Gene Expression....Pages 155-172
Quantification of Cross Hybridization on Oligonucleotide Microarrays....Pages 175-184
Assessing the Potential Effect of Cross-Hybridization on Oligonucleotide Microarrays....Pages 185-198
Who Are Those Strangers in the Latin Square?....Pages 199-208
Bayesian Decomposition Classification of the Project Normal Data Set....Pages 211-231
The Use of Go Terms to Understand the Biological Significance of Microarray Differential Gene Expression Data....Pages 233-247

✦ Subjects

Evolutionary Biology; Artificial Intelligence (incl. Robotics); Human Genetics; Physical Chemistry