Design and Analysis of DNA Microarray Investigations

Name: Design and Analysis of DNA Microarray Investigations
Price: 7.24 USD
Availability: InStock
ISBN: 9780387001357

ISBN-10: 0387001352

ISBN-13: 9780387001357

Edition: 2003

Authors: Richard M. Simon, Edward L. Korn, Lisa M. McShane, Michael D. Radmacher, George W. Wright

List price: $109.99

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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 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…

Book details

List price: $109.99
Copyright year: 2003
Publisher: Springer New York
Publication date: 1/8/2004
Binding: Hardcover
Pages: 200
Size: 6.10" wide x 9.25" long x 0.75" tall
Weight: 1.232
Language: English

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…



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