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Design and Analysis of DNA Microarray Investigations

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Design and Analysis of DNA Microarray Investigations r- -- Richard M. Simon Edward L. Korn Lisa M. McShane Michael D. Radmacher George W. Wright Yingdong Zhao Design and Analysis of DNA Microatray Investigations With 58 Figures, 15 in Full color Springer t, r- -- Richard M. Simon Michael D. Radmacher Edward L. Kom Deparments of Mathematics Lisa M. McShane & Biology George W. Wright Kenyon College Yingdong Zhao Gambier, OR 43022 Biometrc Research Branch National Cancer Institute 9000 Rockvile Pike MSC 7434 Bethesda, MD 20892-7434 Series Editors K. Dietz M.Gail K. Krckeberg Institut fUr Medizinische Biometre National Cancer Institute Le Chatelet Universitat TUbingen Rockvile, MD 20892 F-63270 Manglieu Westbahnhofstrasse 55 USA France D-72070 TUbingen Germany J. Samet A. Tsiatis Departent of Epidemiology Deparment of Statistics School of Public Health Nort Carolina State University Johns Hopkins University Raleigh, NC 27695 615 Wolfe Street USA Baltimore, MD 21205-2103 USA Library of Congress Cataloging-in-Publication Data Design and analysis of DNA microaray investigations / Richard M. Simon. (et al.). p. em. - (Statistics for biology and health) ISBN 0-387-00135-2 (hbk. ; alk. paper) i. DNA microarrays-Statistical methods. i. Simon, Richard M., 1943- II. Senes. 5728'65--c21QP624.5.D726D475 2003 2003054790 ISBN 0-387-00135-2 Printed on acid-free paper. This work was created by U.S. government employees as part of their offcial duties and is a U.S. government work as that term is defined by U.S. Copyright Law. Printed in the United States of America. 987654321 SPIN 10898178 www.springer-ny.com Springer-Verlag New York Berlin Heidelberg A member of BertelsmannSpringer Science+Business Media GmbH -i r- Statistics for Biölogy and Health Series Editors K. Dietz, M. Gail, K. Knckeberg, J. Samet, A. Tsiatis Springer New York Berlin . Heidelberg Hong Kong London Milan Paris Tokyo Acknowledgments We thank our colleagues at the National Cancer Institute who have given us the opportunity to become involved in cancer genomics, and to contribute to discovery of a new generation of therapeutics based on improved knowledge of tumor biology. We particularly thank Dr. Robert Wittes for supporting the establishment of the Molecular Statistics and Bioinformatics Section of the Biometric Research Branch and providing it with the independence to develop expertise, conduct independent research, and establish a unique mul- tidisciplinary environment in which methodologists and experimentalists can interact. We thank Amy Peng Lam for her development of BRB-ArrayTools in collaboration with Richard Simon, and for her many contributions to our microarray analyses. Thanks to Dr. Ming-Chung Li for excellent statistical computing support, to Dr. James P. Brody for permission to use Figure 3.1, to the National Human Genome Research Institute for ilustrations from their Talking Glossary website, to Drs. Tatiana Dracheva, Jin Jen, and Joanna 'Shih for providing Affmetrix GeneChipTM images, to Mr. Erik Marchese at Affymetrix for technical consultation, and to Dr. Laura Lee Johnson for a careful reading of an earlier draft. , ' (\I ~ .. /ÓC Contents Acknowledgments ............................................. v 1 Introduction. 1 2 DNA Microarray Technology.............................. 5 2.1 Overview............................................... 5 2.2 Measuring Label Intensity . 5 2.3 Labeling Methods ... 6 2.4 Printed Microarrays ..................................... 7 2.5 Affymetrix GeneChip ™ Arrays . .. 9 2.6 Other Microarray Platforms. .. .. .. .. 10 3 Design of DNA Microarray Experiments. .. 11 3.1 Introduction............................................ 11 3.2 Study Objectives. .. 12 3.2.1 Class Comparison . .. 12 3.2.2 Class Prediction. .. 13 3.2.3 Class Discovery. .. 13 3.2.4 Pathway Analysis ................................. 13 3.3 Comparing Two RNA Samples. .. .. 13 3.4 Sources of Variation and Levels of ~plication. .. 14 3.5 Pooling of Samples ...................................... 16 3.6 Pairing Samples on Dual-Label Microarrays. .. 17 3.6.1 The Reference Design. .. 17 3.6.2 The Balanced Block Design. .. 19 3.6.3 The Loop Design. .. 20 3.7 Reverse Labeling (Dye Swap) ............................. 21 3.8 Number of Biological Replicates Needed. .. .. 23 vii Contents 4 Image Analysis . .. 29 4.1 Image Generation. .. .. .. .. .. 29 4.2 Image Analysis for cDNA Microarrays ..................... 30 4.2.1 Image Display. .. .. .. 30 4.2.2 Gridding......................................... 30 4.2.3 Segmentation..................................... 31 4.2.4 Foreground Intensity Extraction. .. 32 4.2.5 Background Correction. .. 33 4.2.6 Image Output File. .. 34 4.3 Image Analysis for Affmetrix GeneChip ™ ................ 35 5 Quality Control. .. 39 5.1 Introduction............................................ 39 5.2 Probe-Level Quality Control for Two-Color Arrays. .. 40 5.2.1 Visual Inspection of the Image File. .. .. ... 40 5.2.2 Spots Flagged at Image Analysis ,. .. 40 5.2.3 Spot Size. .. 41 5.2.4 Weak Signal . .. 42 5.2.5 Large Relative Background Intensity. .. 43 5.3 Gene Level Quality Control for Two-Color Arrays ........... 44 5.3.1 Poor Hybridization and Printing.. .. 45 5.3.2 Probe Quality Control Based on Duplicate Spots. .. 45 5.3.3 Low Variance Genes . .. 46 5.4 Array-Level Quality Control for Two-Color Arrays. .. ... 47 5.5 Quality Control for GeneChipTM Arrays. .. 48 5.6 Data Imputation ... .. .. 50 6 Array Normalization..... .. .... .... ............ .. .. ... .... 53 6.1 Introduction............................................ 53 6.2 Choice of Genes for Normalization. .. ... .,. 53 6.2.1 Biologically Defined Housekeeping Genes. .. ... 53 6.2.2 Spiked Controls . .. 54 6.2.3 Normalize Using All Genes ......................... 55 6.2.4 Identification of Housekeeping Genes Based on Observed Data. .. 55 6.3 Normalization Methods for Two-Color Arrays .............. 55 6.3.1 Linear or Global Normalization. .. .. .. ... 56 6.3.2 Intensity-Based Normalization .. .. 57 6.3.3 Location-Based Normalization.. .. .. 59 6.3.4 Combination Location and Intensity Normalization. .. 61 6.4 Normalization of GeneChipTM Arrays. .. .. ... 61 6.4.1 Linear or Global Normalization. .. 61 6.4.2 Intensity-Based Normalization ...................... 62 Contents ix 7 Class Comparison ......................................... 65 7.1 Introduction............................................ 65 7.2 Examining Whether a Single Gene is Differentialy Expressed Between Classes. .. 66 7.2.1 t-Test............;............................... 67 7.2.2 Permutation Tests. .. 68 7.2.3 More Than Two Classes. .. 71 7.2.4 Paired-Specimen Data ............................. 73 7.3 Identifying Which Genes Are Differentially Expressed Between Classes. .. 75 7.3.1 Controllng for No False Positives ................... 76 7.3.2 Controllng the Number of False Positives . .. 80 7.3.3 Controllng the'False Discovery Proportion. .. 81 7.4 Experiments with Very Few Specimens from Each Class. .. 84 7.5 Global Tests of Gene Expression Differences Between Classes. 86 /7.6 Experiments with a Single Specimen from Each Class. .. .. 88 7.7 Regression Model Analysis; Generalizations of Class Comparison ... .. 90 7.8 Evaluating Associations of Gene Expression to Survival. .. 91 7.9 Models for NOlleference Designs on Dual-Label Arrays. .. 92 8 Class Prediction . .. 95 8.1 Introduction............................................ 95 8.2 Feature Selection. .. 97 8.3 Class Prediction Methods ................................ 98 8.3.1 Nomenclature..................................... 98 8.3.2 Discriminant Analysis. .. 98 8.3.3 Variants of Diagonal Linear Discriminant Analysis. .. 101 8.3.4 Nearest Neighbor Classification ..................... 103 8.3.5 Classifcation Trees . 104 8.3.6 Support Vector Machines. 106 8.3.7 Comparison of Methods. 107 8.4 Estimating the Error Rate of the Predictor. ... .. .108 8.4.1 Bias of the Re-Substitution Estimate ................ 108 8.4.2 Cross-Validation and Bootstrap Estimates of Error Rate. .. .. ... .. ..110 8.4.3 Reporting Error Rates . 112 8.4.4 Statistical Signficance pf the Error Rate .. 113 8.45 Validation Dataset ................................113 8.5 Example............................................... 114 8.6 Prognostic Prediction. , . 118 9 Class Discovery. 121 9.1 Introduction............................................ 121 9.2 Simiarity and Distance Metrics . 122 x Contents 9.3 Graphical Displays. .. .. .. .. .. .. .... .. .. .. .. 125 9.3.1 Classical Multidimensional Scaling. .125 9.3.2 Nonmetric Multidimensional Scaling. 131 9.4 Clustering Algorithms. 131 9.4.1 Hierarchical Clustering. 131 9.4.2 k-Means Clustering. 138 9.4.3 Self-Organizing Maps. .. ... .. .. 142 9.4.4 Other Clustering Procedures. 145 9.5 Assessing the Validity of Clusters. 146 9.5.1 Global Tests of Clustering. .,. 148 9.5.2 Estimating the Number of Clusters . 150 9.5.3 Assessing Reproduciblity of Individual Clusters ....... 152 A Basic Biology of Gene Expression. 157 A.l Introduction............................................ 157 B Description of Gene Expression Datasets Used as Examples .. 165 B.l Introduction............................................ 165 B.2 Bittner Melanoma Data. 165 B.3 Luo Prostate Data. 166 B.4 Perou Breast Data. 166 B.5 Tamayo HL-60 Data. 167 B.6 Hedenfalk Breast Cancer Data. .. .. .. .. .. .. 168 C BRB-ArrayTools.... 169 C.1 Software Description. .. 169 C.2 Analysis of Bittner Melanoma Data. 171 C.3 Analysis of Perou Breast Cancer Chemotherapy Data. .178 C.4 Analysis of Hedenfalk Breast Cancer Data. 182 References. ..
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