Analyzing Microarray Gene Expression Data

, by McLachlan, Geoffrey J.; Do, Kim-Anh; Ambroise, Christophe

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ISBN: 9780471226161 | 0471226165
Cover: Hardcover
Copyright: 8/4/2004

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Emphasis on clustering of data of gene tissues * Includes new research findings and activities in molecular biology * Highlights the important general field of bioinformatics and genomics and discusses the impact of microarray analysis on both

Preface

1 Microarrays in Gene Expression Studies

(30)

1.1 Introduction

(1)

1.2 Background Biology

(3)

1.2.1 Genome, Genotype, and Gene Expression

(1)

1.2.2 Of Wild-Types and Other Alleles

(1)

1.2.3 Aspects of Underlying Biology and Physiochemistry

(1)

1.3 Polymerase Chain Reaction

(1)

1.4 cDNA

(1)

1.4.1 Expressed Sequence Tag

(1)

1.5 Microarray Technology and Application

(17)

1.5.1 History of Microarray Development

(2)

1.5.2 Tools of Microarray Technology

(8)

1.5.3 Limitations of Microarray Technology

(2)

1.5.4 Oligonucleotides versus cDNA Arrays

(3)

1.5.5 SAGE: Another Method for Detecting and Measuring Gene Expression Levels

(1)

1.5.6 Emerging Technologies

(1)

1.6 Sampling of Relevant Research Entities and Public Resources

(7)

2 Cleaning and Normalization

(30)

2.1 Introduction

(1)

2.2 Cleaning Procedures

(6)

2.2.1 Image Processing to Extract Information

(4)

2.2.2 Missing Value Estimation

(2)

2.2.3 Sources of Nonlinearity

(1)

2.3 Normalization and Plotting Procedures for Oligonucleotide Arrays

(6)

2.3.1 Global Approaches for Oligonucleotide Array Data

(1)

2.3.2 Spiked Standard Approaches

(2)

2.3.3 Geometric Mean and Linear Regression Normalization for Multiple Arrays

(1)

2.3.4 Nonlinear Normalization for Multiple Arrays Using Smooth Curves

(2)

2.4 Normalization Methods for cDNA Microarray Data

(8)

2.4.1 Single-Array Normalization

(2)

2.4.2 Multiple Slides Normalization

(1)

2.4.3 ANOVA and Related Methods for Normalization

(1)

2.4.4 Mixed-Model Method for Normalization

(1)

2.4.5 SNOMAD

(1)

2.5 Transformations and Replication

(4)

2.5.1 Importance of Replication

(1)

2.5.2 Transformations

(3)

2.6 Analysis of the Alon Data Set

(1)

2.7 Comparison of Normalization Strategies and Discussion

(5)

3 Some Cluster Analysis Methods

(38)

3.1 Introduction

(1)

3.2 Reduction in the Dimension of the Feature Space

(1)

3.3 Cluster Analysis

(1)

3.4 Some Hierarchical Agglomerative Techniques

(4)

3.5 kappa-Means Clustering

(1)

3.6 Cluster Analysis with No A Priori Metric

(1)

3.7 Clustering via Finite Mixture Models

(3)

3.7.1 Definition

(2)

3.7.2 Advantages of Model-Based Clustering

(1)

3.8 Fitting Mixture Models Via the EM Algorithm

(3)

3.8.1 E-Step

(1)

3.8.2 M-Step

(1)

3.8.3 Choice of Starting Values for the EM Algorithm

(1)

3.9 Clustering Via Normal Mixtures

(22)

3.9.1 Heteroscedastic Components

(1)

3.9.2 Homoscedastic Components

(1)

3.9.3 Spherical Components

(1)

3.9.4 Choice of Root

(1)

3.9.5 Available Software

(1)

3.10 Mixtures of τ Distributions

(1)

3.11 Mixtures of Factor Analyzers

(2)

3.12 Choice of Clustering Solution

(1)

3.13 Classification ML Approach

(1)

3.14 Mixture Models for Clinical and Microarray Data

(2)

3.14.1 Unconditional Approach

(1)

3.14.2 Conditional Approach

(1)

3.15 Choice of the Number of Components in a Mixture Model

(2)

3.15.1 Order of a Mixture Model

(1)

3.15.2 Approaches for As ssing Mixture Order

(1)

3.15.3 Bayesian Informatio Criterion

(1)

3.15.4 Integrated Classificati n Likelihood Criterion

(1)

3.16 Resampling Approach

(1)

3.17 Other Resampling Approaches for Number of Clusters

(1)

3.17.1 The Gap Statistic

(1)

3.17.2 The Clest Method for the Number of Clusters

(1)

3.18 Simulation Results for Two Resampling Approaches

(3)

3.19 Principal Component Analysis

(3)

3.19.1 Introduction

(2)

3.19.2 Singular Value Decomposition

(1)

3.19.3 Some Other Multivariate Exploratory Methods

(1)

3.20 Canonical Variate Analysis

(3)

3.20.1 Linear Projections with Group Structure

(1)

3.20.2 Canonical Variates

(2)

3.21 Partial Least Squares

(2)

4 Clustering of Tissue Samples

(34)

4.1 Introduction

(1)

4.2 Notation

100

(1)

4.3 Two Clustering Problems

101

(1)

4.4 Principal Component Analysis

102

(1)

4.5 The EMMIX-GENE Clustering Procedure

103

(1)

4.6 Step 1: Screening of Genes

104

(1)

4.7 Step 2: Clustering of Genes: Formation of Metagenes

105

(2)

4.8 Step 3: Clustering of Tissues

107

(1)

4.9 EMMIX-GENE Software

108

(1)

4.10 Example: Clustering of Alon Data

108

(4)

4.10.1 Clustering on Basis of 446 Genes

108

(1)

4.10.2 Clustering on Basis of Gene Groups

109

(3)

4.10.3 Clustering on Basis of Metagenes

112

(1)

4.11 Example: Clustering of van't Veer Data

112

(12)

4.11.1 Screening and Clustering of Genes

113

(2)

4.11.2 Usefulness of the Selected Genes

115

(6)

4.11.3 Clustering of Tissues

121

(2)

4.11.4 Use of Underlying Signatures with Clinical Data

123

(1)

4.12 Choosing the Number of Clusters in Microarray Data

124

(1)

4.12.1 Some Previous Attempts

124

(1)

4.13 Likelihood Ratio Test Applied to Microarray Data

125

(3)

4.13.1 Golub Data

125

(1)

4.13.2 Alizadeh Data

126

(1)

4.13.3 Bittner Data

127

(1)

4.13.4 van't Veer Data

127

(1)

4.14 Effect of Selection Bias on the Number of Clusters

128

(1)

4.15 Clustering on Microarray arid-Clinical Data

128

(2)

4.16 Discussion

130

(3)

5 Screening and Clustering of Genes

133

(52)

5.1 Detection of Differentially Expressed Genes

133

(4)

5.1.1 Introduction

133

(1)

5.1.2 Fold Change

134

(1)

5.1.3 Multiplicity Problem

134

(1)

5.1.4 Overview of Literature

135

(2)

5.2 Test of a Single Hypothesis

137

(1)

5.3 Gene Statistics

138

(1)

5.3.1 Calculation of Interactions via ANOVA Models

138

(1)

5.3.2 Two-Sample τ-Statistics

139

(1)

5.4 Multiple Hypothesis Testing

139

(5)

5.4.1 Outcomes with Multiple Hypotheses

140

(1)

5.4.2 Controlling the FWER

140

(1)

5.4.3 False Discovery Rate (FDR)

141

(1)

5.4.4 Benjamini-Hochberg Procedure

142

(1)

5.4.5 False Nondiscovery Rate (FNR)

143

(1)

5.4.6 Positive FDR

143

(1)

5.4.7 Positive FNR

143

(1)

5.4.8 Linking False Rates with Posterior Probabilities

143

(1)

5.5 Null Distribution of Test Statistic

144

(4)

5.5.1 Permutation Method

144

(1)

5.5.2 Null Replications of the Test Statistic

145

(1)

5.5.3 The SAM Method

146

(1)

5.5.4 Application of SAM Method to Alon Data

146

(2)

5.6 Recent Approaches for Strong Control of the FDR

148

(6)

5.6.1 The q-Value

148

(1)

5.6.2 Technical Definition of q-Value

149

(1)

5.6.3 Controlling FDR Strongly

150

(1)

5.6.4 Selecting Genes via the q-Value

151

(1)

5.6.5 Application to Hedenfalk Data

152

(2)

5.7 Two-Component Mixture Model Framework

154

(4)

5.7.1 Definition of Model

154

(1)

5.7.2 Bayes Rule

155

(1)

5.7.3 Estimated FDR

155

(1)

5.7.4 Bayes Risk in terms of Estimated FDR and FNR

156

(2)

5.8 Nonparametric Empirical Bayes Approach

158

(2)

5.8.1 Method of Efron et al. (2001)

158

(1)

5.8.2 Mixture Model Method (MMM)

158

(1)

5.8.3 Nonparametric Bayesian Approach

159

(1)

5.8.4 Application of Empirical Bayes Methods to Alon Data

159

(1)

5.9 Parametric Mixture Models for Differential Gene Expression

160

(6)

5.9.1 Parametric Empirical Bayes Methods

160

(4)

5.9.2 Finding Clusters of Differentially Expressed Genes

164

(1)

5.9.3 Example: Fitting Normal Mixtures to τ-Statistic Values

165

(1)

5.10 Use of the Rho-Value as a Summary Statistic

166

(5)

5.10.1 Beta Mixture for Distribution of Rho-Values

168

(1)

5.10.2 Example: Fitting Beta Mixtures to Rho-Values

169

(2)

5.11 Clustering of Genes

171

(2)

5.12 Finding Correlated Genes

173

(1)

5.13 Clustering of Genes via Full Expression Profiles

173

(1)

5.14 Clustering of Genes via PCA of Expression Profiles

174

(1)

5.15 Clustering of Genes with Repeated Measurements

175

(2)

5.15.1 A Mixture Model for Technical Replicates

175

(1)

5.15.2 Application of EM Algorithm

176

(1)

5.15.3 M-Step

176

(1)

5.16 Gene Shaving

177

(8)

5.16.1 Introduction

177

(1)

5.16.2 Methodology and implementation

177

(1)

5.16.3 Optimal cluster size via the Gap statistic

178

(1)

5.16.4 Supervised Gene Shaving

179

(1)

5.16.5 Real Data Example

179

(1)

5.16.6 Computer Software

180

(5)

6 Discriminant Analysis

185

(36)

6.1 Introduction

185

(1)

6.2 Basic Notation

185

(2)

6.3 Error Rates

187

(1)

6.4 Decision-Theoretic Approach

187

(2)

6.5 Training Data

189

(1)

6.6 Different Types of Error Rates

190

(1)

6.7 Sample-Based Discriminant Rules

191

(1)

6.8 Parametric Discriminant Rules

192

(1)

6.9 Discrimination via Normal Models

193

(6)

6.9.1 Heteroscedastic Normal Model

193

(1)

6.9.2 Plug-in Sample NQDR

194

(1)

6.9.3 Homoscedastic Normal Model

195

(2)

6.9.4 Optimal Error Rates

197

(1)

6.9.5 Plug-in Sample NLDR

197

(1)

6.9.6 Normal Mixture Model

198

(1)

6.10 Fisher's Linear Discriminant Function

199

(2)

6.10.1 Separation Approach

199

(1)

6.10.2 Regression Approach

199

(2)

6.11 Logistic Discrimination

201

(1)

6.12 Nearest-Centroid Rule

202

(1)

6.13 Support Vector Machines

203

(4)

6.13.1 Two Classes

203

(1)

6.13.2 Selection of Feature Variables

204

(1)

6.13.3 Multiple Classes

205

(1)

6.13.4 Computer Software

206

(1)

6.14 Variants of Support Vector Machines

207

(1)

6.15 Neural Networks

207

(1)

6.16 Nearest-Neighbor Rules

208

(2)

6.16.1 Introduction

208

(1)

6.16.2 Definition of a kappa-NN Rule

209

(1)

6.17 Classification Trees

210

(1)

6.18 Error-Rate Estimation

211

(2)

6.18.1 Apparent Error Rate

211

(2)

6.18.2 Bias Correction of the Apparent Error Rate

213

(1)

6.19 Cross-Validation

213

(1)

6.19.1 Leave-One-Out(LOO) Estimator

213

(1)

6.19.2 q-Fold Cross-Validation

214

(1)

6.20 Error-Rate Estimation via the Bootstrap

214

(2)

6.20.1 The 0.632 Estimator

214

(1)

6.20.2 Mean Squared Error of the Estimated Error Rate

215

(1)

6.21 Selection of Feature Variables

216

(2)

6.22 Error-Rate Estimation with Selection Bias

218

(3)

6.22.1 Selection Bias

218

(1)

6.22.2 External Cross-Validation

218

(1)

6.22.3 The 0.632+ Estimator

219

(2)

7 Supervised Classification of Tissue Samples

221

(32)

7.1 Introduction

221

(1)

7.2 Reducing the Dimension of the Feature Space of Genes

222

(2)

7.2.1 Principal Components

223

(1)

7.2.2 Partial Least Squares

223

(1)

7.2.3 Ranking of Genes

223

(1)

7.2.4 Grouping of Genes

224

(1)

7.3 SVM with Recursive Feature Elimination (RFE)

224

(2)

7.4 Selection Bias: SVM with RFE

226

(2)

7.5 Selection Bias: Fisher's Rule with Forward Selection

228

(2)

7.6 Selection Bias: Noninformative Data

230

(2)

7.7 Discussion of Selection Bias

232

(1)

7.8 Selection of Marker Genes with SVM

233

(3)

7.8.1 Description of van de Vijver Breast Cancer Data

233

(1)

7.8.2 Application of SVM with RFE

234

(2)

7.9 Nearest-Shrunken Centroids

236

(3)

7.9.1 Definition

236

(3)

7.10 Comparison of Nearest-Shrunken Centroids with SVM

239

(6)

7.10.1 Alon Data

239

(1)

7.10.2 van de Vijver Data

239

(6)

7.11 Selection Bias Working with the Top 70 Genes

245

(4)

7.11.1 Bias in Error Rates

245

(1)

7.11.2 Bias in Comparative Studies of Error Rates

246

(2)

7.11.3 Bias in Plots

248

(1)

7.12 Discriminant Rules Via Initial Grouping of Genes

249

(4)

7.12.1 Supervised Version of EMMIX-GENE

249

(1)

7.12.2 Bayesian Tree Classification

249

(1)

7.12.3 Tree Harvesting

249

(1)

7.12.4 Block PCA

250

(1)

7.12.5 Grouping of Genes via Supervised Procedures

250

(3)

8 Linking Microarray Data with Survival Analysis

253

(14)

8.1 Introduction

253

(1)

8.2 Four Lung Cancer Data Sets

254

(1)

8.3 Statistical Analysis of Two Data Sets

255

(1)

8.4 Ontario Data set

256

(5)

8.4.1 Cluster Analysis

256

(3)

8.4.2 Survival Analysis

259

(1)

8.4.3 Discriminant Analysis

260

(1)

8.5 Stanford Data Set

261

(5)

8.5.1 Cluster Analysis of AC Tumors

262

(1)

8.5.2 Survival Analysis

263

(3)

8.5.3 Discriminant Analysis

266

(1)

8.6 Discussion

266

(1)

References

267

(30)

Author Index

297

(16)

Subject Index

313

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Analyzing Microarray Gene Expression Data

Summary

Author Biography

Table of Contents

Supplemental Materials