Design and Analysis of DNA Microarray Investigations

ISBN-10: 0387001352
ISBN-13: 9780387001357
Edition: 2003
List price: $149.00 Buy it from $36.90
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Description: This book is targeted to biologists with limited statistical background and to statisticians and computer scientists interested in being effective collaborators on multi-disciplinary DNA microarray projects. State-of-the-art analysis methods are  More...

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Book details

List price: $149.00
Copyright year: 2003
Publisher: Springer
Publication date: 1/8/2004
Binding: Hardcover
Pages: 200
Size: 6.00" wide x 9.50" long x 0.75" tall
Weight: 1.034
Language: English

This book is targeted to biologists with limited statistical background and to statisticians and computer scientists interested in being effective collaborators on multi-disciplinary DNA microarray projects. State-of-the-art analysis methods are presented with minimal mathematical notation and a focus on concepts. This book is unique because it is authored by statisticians at the National Cancer Institute who are actively involved in the application of microarray technology.Many laboratories are not equipped to effectively design and analyze studies that take advantage of the promise of microarrays. Many of the software packages available to biologists were developed without involvement of statisticians experienced in such studies and contain tools that may not be optimal for particular applications. This book provides a sound preparation for designing microarray studies that have clear objectives, and for selecting analysis tools and strategies that provide clear and valid answers. The book offers an in depth understanding of the design and analysis of experiments utilizing microarrays and should benefit scientists regardless of what software packages they prefer. In order to provide all readers with hands on experience in data analysis, it includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is freely available from the National Cancer Institute for non-commercial use.The authors are current or former members of the Biometric Research Branch at the National Cancer Institute.nbsp; They have collaborated on major biomedical studies utilizing microarrays and in the development of statistical methodology for the design and analysis of microarray investigations. Dr. Simon, chief of the branch, is also the architect of BRB-ArrayTools.

Dr Richard M. Simon is chief of the Biometric Research Branch of the National Cancer Institute, where he is chief statistician for the Division of Cancer Treatment and Diagnosis. He is the lead author of the textbook Design and Analysis of DNA Microarray Experiments and has more than 450 publications. Simon has been influential in promoting excellence in clinical trial design and analysis. He has served on the Oncologic Advisory Committee of the US Food and Drug Administration and is a frequent advisor to government, academic and industry organizations involved with developing improved treatments and diagnostics for patients with cancer. In 1998 Dr Simon established the Molecular Statistics and Bioinformatics Section of the National Cancer Institute, a multidisciplinary group of scientists developing and applying methods for the application of genomics to cancer therapeutics. He is the architect of BRB-ArrayTools software for the analysis of microarray expression and copy number data.

Acknowledgments
Introduction
DNA Microarray Technology
Overview
Measuring Label Intensity
Labeling Methods
Printed Microarrays
Affymetrix GeneChip Arrays
Other Microarray Platforms
Design of DNA Microarray Experiments
Introduction
Study Objectives
Class Comparison
Class Prediction
Class Discovery
Pathway Analysis
Comparing Two RNA Samples
Sources of Variation and Levels of Replication
Pooling of Samples
Pairing Samples on Dual-Label Microarrays
The Reference Design
The Balanced Block Design
The Loop Design
Reverse Labeling (Dye Swap)
Number of Biological Replicates Needed
Image Analysis
Image Generation
Image Analysis for cDNA Microarrays
Image Display
Gridding
Segmentation
Foreground Intensity Extraction
Background Correction
Image Output File
Image Analysis for Affymetrix GeneChip
Quality Control
Introduction
Probe-Level Quality Control for Two-Color Arrays
Visual Inspection of the Image File
Spots Flagged at Image Analysis
Spot Size
Weak Signal
Large Relative Background Intensity
Gene Level Quality Control for Two-Color Arrays
Poor Hybridization and Printing
Probe Quality Control Based on Duplicate Spots
Low Variance Genes
Array-Level Quality Control for Two-Color Arrays
Quality Control for GeneChip Arrays
Data Imputation
Array Normalization
Introduction
Choice of Genes for Normalization
Biologically Defined Housekeeping Genes
Spiked Controls
Normalize Using All Genes
Identification of Housekeeping Genes Based on Observed Data
Normalization Methods for Two-Color Arrays
Linear or Global Normalization
Intensity-Based Normalization
Location-Based Normalization
Combination Location and Intensity Normalization
Normalization of GeneChip Arrays
Linear or Global Normalization
Intensity-Based Normalization
Class Comparison
Introduction
Examining Whether a Single Gene is Differentially Expressed Between Classes
t-Test
Permutation Tests
More Than Two Classes
Paired-Specimen Data
Identifying Which Genes Are Differentially Expressed Between Classes
Controlling for No False Positives
Controlling the Number of False Positives
Controlling the False Discovery Proportion
Experiments with Very Few Specimens from Each Class
Global Tests of Gene Expression Differences Between Classes
Experiments with a Single Specimen from Each Class
Regression Model Analysis; Generalizations of Class Comparison
Evaluating Associations of Gene Expression to Survival
Models for Nonreference Designs on Dual-Label Arrays
Class Prediction
Introduction
Feature Selection
Class Prediction Methods
Nomenclature
Discriminant Analysis
Variants of Diagonal Linear Discriminant Analysis
Nearest Neighbor Classification
Classification Trees
Support Vector Machines
Comparison of Methods
Estimating the Error Rate of the Predictor
Bias of the Re-Substitution Estimate
Cross-Validation and Bootstrap Estimates of Error Rate
Reporting Error Rates
Statistical Significance of the Error Rate
Validation Dataset
Example
Prognostic Prediction
Class Discovery
Introduction
Similarity and Distance Metrics
Graphical Displays
Classical Multidimensional Scaling
Nonmetric Multidimensional Scaling
Clustering Algorithms
Hierarchical Clustering
k-Means Clustering
Self-Organizing Maps
Other Clustering Procedures
Assessing the Validity of Clusters
Global Tests of Clustering
Estimating the Number of Clusters
Assessing Reproduciblity of Individual Clusters
Basic Biology of Gene Expression
Introduction
Description of Gene Expression Datasets Used as Examples
Introduction
Bittner Melanoma Data
Luo Prostate Data
Perou Breast Data
Tamayo HL-60 Data
Hedenfalk Breast Cancer Data
BRB-ArrayTools
Software Description
Analysis of Bittner Melanoma Data
Analysis of Perou Breast Cancer Chemotherapy Data
Analysis of Hedenfalk Breast Cancer Data
References
Index

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