Microarray bioinformatics

This book is a comprehensive guide to all of the mathematics, statistics and computing you will need to successfully operate DNA microarray experiments. It is written for researchers, clinicians, laboratory heads and managers, from both biology and bioinformatics backgrounds, who work with, or who intend to work with microarrays. The book covers all aspects of microarray bioinformatics, giving you the tools to design arrays and experiments, to analyze your data, and to share your results with your organisation or with the international community. There are chapters covering sequence databases, oligonucleotide design, experimental design, image processing, normalisation, identifying differentially expressed genes, clustering, classification and data standards. The book is based on the highly successful Microarray Bioinformatics course at Oxford University, and therefore is ideally suited for teaching the subject at postgraduate or professional level. Half-title......Page 3 Title......Page 5 Copyright......Page 6 Dedication......Page 7 Contents......Page 9 Foreword......Page 11 Preface......Page 13 Outline of Contents......Page 14 Acknowledgments......Page 15 MICROARRAY BIOINFORMATICS......Page 17 SECTION 1.2 MAKING MICROARRAYS......Page 19 Spotted Microarrays......Page 21 In-Situ Synthesised Oligonucleotide Arrays......Page 23 Maskless Photodeprotection Technology......Page 24 Synthesis Yields......Page 25 SECTION 1.3 USING MICROARRAYS......Page 27 Sample Preparation and Labelling......Page 28 Hybridisation......Page 29 Washing......Page 31 Image Acquisition......Page 32 KEY POINTS SUMMARY......Page 34 SCANNERS......Page 36 SECTION 2.1 INTRODUCTION......Page 37 SECTION 2.2 PRIMARY SEQUENCE DATABASES......Page 41 SECTION 2.3 SECONDARY SEQUENCE DATABASES......Page 45 UniGene......Page 46 The TIGR Gene Indices......Page 51 RefSeq......Page 52 Ensembl......Page 55 Microbial Genomes......Page 56 Yeast......Page 58 INTERNET RESOURCES......Page 59 RESEARCH PAPERS......Page 60 What Makes a Good Oligonucleotide Probe?......Page 61 RepeatMasker......Page 63 SECTION 3.3 PREDICTION OF CROSS-HYBRIDISATION TO RELATED GENES......Page 65 SECTION 3.4 THE THERMODYNAMICS OF NUCLEIC ACID DUPLEXES AND THE PREDICTION OF MELTING TEMPERATURE......Page 70 Base-Stacking and Initiation Parameters......Page 71 Adjustments for Salt Concentrations......Page 73 SECTION 3.5 PROBE SECONDARY STRUCTURE......Page 74 Computation of Probe Secondary Structure......Page 75 Using Mfold over the Web......Page 76 KEY POINTS SUMMARY......Page 77 Software Available for Download from the Internet......Page 78 SECTION 4.2 FEATURE EXTRACTION......Page 80 Identifying the Positions of the Features......Page 82 Fixed Circle Segmentation......Page 84 Histogram Segmentation......Page 85 Identifying the Background Pixels......Page 86 Calculation of Numerical Information......Page 87 Books......Page 90 Removing Flagged Features......Page 91 Affymetrix Data......Page 92 Taking Logarithms......Page 93 SECTION 5.3 WITHIN-ARRAY NORMALISATION......Page 98 Linear Regression of Log Ratio Against Average Intensity......Page 99 Nonlinear Regression of Log Ratio Against Average Intensity......Page 102 Two-Dimensional Loess Regression......Page 105 Block-by-Block Loess Regression......Page 107 SECTION 5.4 BETWEEN-ARRAY NORMALISATION......Page 108 Visualising the Data: Box Plots......Page 111 Distribution Normalisation......Page 114 Data Set 5B......Page 115 Useful Normalisation Resources......Page 116 Calibration Experiments......Page 118 Pilot Studies......Page 119 Log-Normal Distribution......Page 120 Method for Measuring Variability......Page 121 Variation Between Replicate Features on an Array......Page 122 Variability Between Hybridisations to Different Arrays......Page 124 KEY POINTS SUMMARY......Page 126 Data Set 6B......Page 127 SECTION 7.1 INTRODUCTION......Page 128 Statistical Inference......Page 130 Hypothesis Tests and P-values......Page 131 Independence......Page 132 Paired or One-Sample t-Test......Page 133 Unpaired or Two-Sample t-Test......Page 135 Requirements of t-Tests......Page 137 SECTION 7.4 NON-PARAMETRIC STATISTICS......Page 141 Classical Non-parametric Statistics......Page 142 Bootstrap Analyses......Page 145 SECTION 7.5 MULTIPLICITY OF TESTING......Page 156 Estimation of False Positive Rate......Page 157 Bonferroni Adjustment......Page 159 The One-Way ANOVA......Page 160 Multifactor ANOVAs......Page 161 Data Set 7A......Page 162 Publications for Microarray Analysis Methods......Page 163 Internet Resources......Page 164 SECTION 8.1 INTRODUCTION......Page 165 SECTION 8.2 SIMILARITY OF GENE OR SAMPLE PROFILES......Page 166 Features of a Distance Measure......Page 167 Correlation Coefficient......Page 168 Spearman Correlation......Page 171 Euclidean Distance......Page 174 SECTION 8.3 DIMENSIONALITY REDUCTION......Page 177 Principal Component Analysis......Page 178 How PCA Works......Page 179 Multidimensional Scaling......Page 181 SECTION 8.4 HIERARCHICAL CLUSTERING......Page 184 Linkage Methods......Page 189 Isomorphisms......Page 193 SECTION 8.5 THE RELIABILITY AND ROBUSTNESS OF HIERARCHICAL CLUSTERING......Page 194 Parametric Bootstrapping......Page 195 Construction of a Consensus Tree......Page 197 The K-Means Algorithm......Page 200 How to choose a good value of k?......Page 201 Validating K-Means Clustering......Page 202 Choosing the Size of a Self-Organised Map......Page 203 Validation of Self-Organised Maps......Page 205 KEY POINTS SUMMARY......Page 206 Internet Resources......Page 207 SECTION 9.1 INTRODUCTION......Page 209 Separability......Page 210 Number of Classes......Page 213 K-Nearest Neighbours......Page 215 Linear Discriminant Analysis......Page 217 Support Vector Machines......Page 219 Training and Test Sets......Page 221 Cross-Validation......Page 223 SECTION 9.4 DIMENSIONALITY REDUCTION......Page 224 Principal Component Analysis......Page 226 Individual Gene Selection......Page 228 Pairwise Gene Selection......Page 230 Voting Algorithms......Page 231 Genetic Algorithms......Page 232 The Algorithm......Page 233 KEY POINTS SUMMARY......Page 234 Data Set 9B......Page 235 Books......Page 236 SECTION 10.2 BLOCKING, RANDOMISATION AND BLINDING......Page 237 Bias, Randomisation and Blinding......Page 239 SECTION 10.3 CHOICE OF TECHNOLOGY AND ARRANGEMENT OF SAMPLES......Page 240 Estimating Variability......Page 242 Confounding and Colour Swaps......Page 243 SECTION 10.4 HOW MANY REPLICATES?......Page 247 Confidence and Power......Page 248 Types of Replicates......Page 249 Standard Deviation......Page 250 Power Analysis Calculation and Tables......Page 251 Papers from Which We Have Used Data or Experimental Design Ideas......Page 255 SECTION 11.1 INTRODUCTION......Page 257 Local Data Warehousing......Page 259 Data Analysis and Visualisation......Page 261 The Need for Standards......Page 263 The Microarray Gene Expression Data Society (MGED)......Page 264 Array Design Description......Page 265 Experiment Description......Page 267 SECTION 11.4 ONTOLOGIES......Page 270 Gene Ontologies and the GO Consortium......Page 271 Properties......Page 275 Individuals......Page 276 Public Microarray Databases......Page 277 Useful Papers......Page 278 APPENDIX MIAME Glossary......Page 279 INDEX......Page 285

DNA microarrays have revolutionized molecular biology and are becoming a standard tool in the field. Dov Stekel's book is a comprehensive guide to the mathematics, statistics, and computing required to use microarrays successfully. Unlike traditional molecular biology, the successful use of DNA microarrays requires the application of statistics and computing to design the arrays and experiments, and to analyze and manage the data. This book is written for researchers, clinicians, and laboratory managers.

"This book is a comprehensive guide to all of the mathematics, statistics and computing you will need to successfully operate DNA microarray experiments ... covers all aspects of microarray bioinformatics, giving you the tools to design arrays and experiments, to analyze your data, and to share your results with your organisation or with the international community."--Jacket DNA microarrays have revolutionised molecular biology and are becoming a standard tool in the field. Written for those from both laboratory and bioinformatics backgrounds, this book is a comprehensive guide to all of the mathematics, statistics and computing necessary to successfully carry out and analyze DNA microarray experiments.