Statistics for Microarrays Design, Analysis

Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data - from getting good data to obtaining meaningful results. Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference. Features many examples throughout using real data from microarray experiments. Computational techniques are integrated into the text. Takes a very practical approach, suitable for statistically-minded biologists. Supported by a Website featuring colour images, software, and data sets. Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.

Ernst Wit is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.

John McClure is the author of Statistics for Microarrays: Design, Analysis and Inference, published by Wiley.

Preface

xi

1 Preliminaries

1

(112)

1.1 Using the R Computing Environment

1

(2)

1.1.1 Installing smida

2

(1)

1.1.2 Loading smida

3

(1)

1.2 Data Sets from Biological Experiments

3

(132)

1.2.1 Arabidopsis experiment: Anna Amtmann

4

(2)

1.2.2 Skin cancer experiment: Nighean Barr

6

(1)

1.2.3 Breast cancer experiment: John Bartlett

7

(2)

1.2.4 Mammary gland experiment: Gusterson group

9

(1)

1.2.5 Tuberculosis experiment: ΒμG@S group

10

(103)

I Getting Good Data

113

(22)

2 Set-up of a Microarray Experiment

15

(8)

2.1 Nucleic Acids: DNA and RNA

15

(1)

2.2 Simple cDNA Spotted Microarray Experiment

16

(7)

2.2.1 Growing experimental material

17

(1)

2.2.2 Obtaining RNA

17

(1)

2.2.3 Adding spiking RNA and poly-T primer

18

(1)

2.2.4 Preparing the enzyme environment

19

(1)

2.2.5 Obtaining labelled cDNA

19

(1)

2.2.6 Preparing cDNA mixture for hybridization

19

(1)

2.2.7 Slide hybridization

20

(3)

3 Statistical Design of Microarrays

23

(34)

3.1 Sources of Variation

24

(2)

3.2 Replication

26

(10)

3.2.1 Biological and technical replication

27

(2)

3.2.2 How many replicates?

29

(1)

3.2.3 Pooling samples

30

(6)

3.3 Design Principles

36

(4)

3.3.1 Blocking, crossing and randomization

37

(2)

3.3.2 Design and normalization

39

(1)

3.4 Single-channel Microarray Design

40

(4)

3.4.1 Design issues

41

(1)

3.4.2 Design layout

42

(1)

3.4.3 Dealing with technical replicates

42

(2)

3.5 Two-channel Microarray Designs

44

(13)

3.5.1 Optimal design of dual-channel arrays

44

(6)

3.5.2 Several practical two-channel designs

50

(7)

4 Normalization

57

(46)

4.1 Image Analysis

57

(5)

4.1.1 Filtering

58

(2)

4.1.2 Gridding

60

(1)

4.1.3 Segmentation

61

(1)

4.1.4 Quantification

62

(1)

4.2 Introduction to Normalization

62

(7)

4.2.1 Scale of gene expression data

63

(2)

4.2.2 Using control spots for normalization

65

(1)

4.2.3 Missing data

65

(4)

4.3 Normalization for Dual-channel Arrays

69

(24)

4.3.1 Order for the normalizations

70

(1)

4.3.2 Spatial correction

71

(5)

4.3.3 Background correction

76

(4)

4.3.4 Dye effect normalization

80

(4)

4.3.5 Normalization within and across conditions

84

(9)

4.4 Normalization of Single-channel Arrays

93

(10)

4.4.1 Affymetrix data structure

93

(1)

4.4.2 Normalization of Affymetrix data

94

(9)

5 Quality Assessment

103

(22)

5.1 Using MIAME in Quality Assessment

104

(1)

5.1.1 Components of MIAME

104

(1)

5.2 Comparing Multivariate Data

105

(8)

5.2.1 Measurement scale

105

(1)

5.2.2 Dissimilarity and distance measures

106

(5)

5.2.3 Representing multivariate data

111

(2)

5.3 Detecting Data Problems

113

(10)

5.3.1 Clerical errors

114

(3)

5.3.2 Normalization problems

117

(2)

5.3.3 Hybridization problems

119

(2)

5.3.4 Array mishandling

121

(2)

5.4 Consequences of Quality Assessment Checks

123

(2)

6 Microarray Myths: Data

125

(12)

6.1 Design

125

(4)

6.1.1 Single-versus dual-channel designs?

125

(4)

6.1.2 Dye-swap experiments

129

(1)

6.2 Normalization

129

(8)

6.2.1 Myth: 'microarray data is Gaussian'

129

(2)

6.2.2 Myth: 'microarray data is not Gaussian'

131

(1)

6.2.3 Confounding spatial and dye effect

132

(1)

6.2.4 Myth: 'non-negative background subtraction'

133

(2)

II Getting Good Answers

135

(116)

7 Microarray Discoveries

137

(40)

7.1 Discovering Sample Classes

137

(18)

7.1.1 Why cluster samples?

138

(1)

7.1.2 Sample dissimilarity measures

139

(5)

7.1.3 Clustering methods for samples

144

(11)

7.2 Exploratory Supervised Learning

155

(5)

7.2.1 Labelled dendrograms

156

(1)

7.2.2 Labelled PAM-type clusterings

157

(3)

7.3 Discovering Gene Clusters

160

(17)

7.3.1 Similarity measures for expression profiles

160

(3)

7.3.2 Gene clustering methods

163

(14)

8 Differential Expression

177

(34)

8.1 Introduction

177

(2)

8.1.1 Classical versus Bayesian hypothesis testing

177

(2)

8.1.2 Multiple testing 'problem'

179

(1)

8.2 Classical Hypothesis Testing

179

(17)

8.2.1 What is a hypothesis test?

180

(3)

8.2.2 Hypothesis tests for two conditions

183

(9)

8.2.3 Decision rules

192

(3)

8.2.4 Results from skin cancer experiment

195

(1)

8.3 Bayesian Hypothesis Testing

196

(15)

8.3.1 A general testing procedure

200

(3)

8.3.2 Bayesian τ-test

203

(8)

9 Predicting Outcomes with Gene Expression Profiles

211

(36)

9.1 Introduction

211

(7)

9.1.1 Probabilistic classification theory

212

(5)

9.1.2 Modelling and predicting continuous variables

217

(1)

9.2 Curse of Dimensionality: Gene Filtering

218

(5)

9.2.1 Use only significantly expressed genes

218

(2)

9.2.2 PCA and gene clustering

220

(2)

9.2.3 Penalized methods

222

(1)

9.2.4 Biological selection

222

(1)

9.3 Predicting Class Memberships

223

(12)

9.3.1 Variance-bias trade-off in prediction

223

(4)

9.3.2 Linear discriminant analysis

227

(4)

9.3.3 kappa-nearest neighbour classification

231

(4)

9.4 Predicting Continuous Responses

235

(12)

9.4.1 Penalized regression: LASSO

235

(8)

9.4.2 kappa-nearest neighbour regression

243

(4)

10 Microarray Myths: Inference

247

(4)

10.1 Differential Expression

247

(2)

10.1.1 Myth: 'Bonferroni is too conservative'

247

(1)

10.1.2 FPR and collective multiple testing

248

(1)

10.1.3 Misinterpreting FDR

248

(1)

10.2 Prediction and Learning

249

(2)

10.2.1 Cross-validation

249

(2)

Bibliography

251

(8)

Index

259

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Statistics for Microarrays Design, Analysis and Inference

Summary

Author Biography

Table of Contents

Supplemental Materials