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Contents
Preface v
List of Contributors xi
1 The ALL Dataset 1
F. Hahne and R. Gentleman
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The ALL data . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Data subsetting . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Nonspecific filtering . . . . . . . . . . . . . . . . . . . . . . 3
1.5 BCR/ABL ALL1/AF4 subset . . . . . . . . . . . . . . . . 4
2 R and Bioconductor Introduction 5
R. Gentleman, F. Hahne, S. Falcon,
and M. Morgan
2.1 Finding help in R . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Working with packages . . . . . . . . . . . . . . . . . . . . 7
2.3 Some basic R . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Structures for genomic data . . . . . . . . . . . . . . . . . . 11
2.5 Graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3 Processing Affymetrix Expression Data 25
R. Gentleman and W. Huber
3.1 The input data: CEL files . . . . . . . . . . . . . . . . . . . 25
3.2 Quality assessment . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4 Ranking and filtering probe sets . . . . . . . . . . . . . . . 33
3.5 Advanced preprocessing . . . . . . . . . . . . . . . . . . . . 40
4 Two-Color Arrays 47
Florian Hahne and Wolfgang Huber
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2 Data import . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.3 Image plots . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
viii Contents
4.4 Normalization. . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.5 Differential expression . . . . . . . . . . . . . . . . . . . . . 57
5 Fold-Changes, Log-Ratios, Background Correction,
Shrinkage Estimation, and Variance Stabilization 63
W. Huber
5.1 Fold-changes and (log-)ratios . . . . . . . . . . . . . . . . . 63
5.2 Background-correction and generalized logarithm . . . . . 65
5.3 Calling VSN . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4 How does VSN work? . . . . . . . . . . . . . . . . . . . . . 72
5.5 Robust fitting and the “most genes not differentially
expressed”assumption . . . . . . . . . . . . . . . . . . . . . 74
5.6 Single-color normalization . . . . . . . . . . . . . . . . . . . 78
5.7 The interpretation of glog-ratios . . . . . . . . . . . . . . . 79
5.8 Reference normalization . . . . . . . . . . . . . . . . . . . . 81
6 Easy Differential Expression 83
F. Hahne and W. Huber
6.1 Example data . . . . . . . . . . . . . . . . . . . . . . . . . . 83
6.2 Nonspecific filtering . . . . . . . . . . . . . . . . . . . . . . 84
6.3 Differential expression . . . . . . . . . . . . . . . . . . . . . 85
6.4 Multiple testing correction . . . . . . . . . . . . . . . . . . 87
7 Differential Expression 89
W. Huber, D. Scholtens, F. Hahne,
and A. von Heydebreck
7.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
7.2 Nonspecific filtering . . . . . . . . . . . . . . . . . . . . . . 90
7.3 Differential expression . . . . . . . . . . . . . . . . . . . . . 92
7.4 Multiple testing . . . . . . . . . . . . . . . . . . . . . . . . 94
7.5 Moderated test statistics and the limma package . . . . . 95
7.6 Gene selection by Receiver Operator Characteristic
(ROC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
7.7 When power increases . . . . . . . . . . . . . . . . . . . . . 101
8 Annotation and Metadata 103
W. Huber and F. Hahne
8.1 Our data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
8.2 Multiple probe sets per gene . . . . . . . . . . . . . . . . . 106
8.3 Categories and overrepresentation . . . . . . . . . . . . . . 107
8.4 Working with GO . . . . . . . . . . . . . . . . . . . . . . . 109
8.5 Other annotations available . . . . . . . . . . . . . . . . . . 112
8.6 biomaRt . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
8.7 Database versions of annotation packages . . . . . . . . . . 115
Contents ix
9 Supervised Machine Learning 121
R. Gentleman, W. Huber, and V. J. Carey
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 121
9.2 The example dataset . . . . . . . . . . . . . . . . . . . . . . 123
9.3 Feature selection and standardization . . . . . . . . . . . . 124
9.4 Selecting a distance . . . . . . . . . . . . . . . . . . . . . . 124
9.5 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . 126
9.6 Cross-validation . . . . . . . . . . . . . . . . . . . . . . . . 129
9.7 Randomforests . . . . . . . . . . . . . . . . . . . . . . . . . 132
9.8 Multigroup classification . . . . . . . . . . . . . . . . . . . 135
10 Unsupervised Machine Learning 137
R. Gentleman and V. J. Carey
10.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . 137
10.2 Distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
10.3 How many clusters? . . . . . . . . . . . . . . . . . . . . . . 142
10.4 Hierarchical clustering . . . . . . . . . . . . . . . . . . . . . 144
10.5 Partitioningmethods . . . . . . . . . . . . . . . . . . . . . 146
10.6 Self-organizingmaps . . . . . . . . . . . . . . . . . . . . . . 148
10.7 Hopach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
10.8 Silhouette plots . . . . . . . . . . . . . . . . . . . . . . . . . 152
10.9 Exploring transformations . . . . . . . . . . . . . . . . . . 154
10.10 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
11 Using Graphs for Interactome Data 159
T. Chiang, S. Falcon, F. Hahne, and W. Huber
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 159
11.2 Exploring the protein interaction graph . . . . . . . . . . . 160
11.3 The co-expression graph . . . . . . . . . . . . . . . . . . . . 162
11.4 Testing the association between physical interaction and
coexpression . . . . . . . . . . . . . . . . . . . . . . . . . . 164
11.5 Some harder problems . . . . . . . . . . . . . . . . . . . . . 165
11.6 Reading PSI-25 XML files from IntAct
with the Rintact package . . . . . . . . . . . . . . . . . . 165
12 Graph Layout 173
F. Hahne, W. Huber, and R. Gentleman
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 173
12.2 Layout and rendering using Rgraphviz . . . . . . . . . . 175
12.3 Directed graphs . . . . . . . . . . . . . . . . . . . . . . . . . 180
12.4 Subgraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
12.5 Tooltips and hyperlinks on graphs . . . . . . . . . . . . . . 187
x Contents
13 Gene Set Enrichment Analysis 193
R. Gentleman, M. Morgan, and W. Huber
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 193
13.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 196
13.3 Identifying and assessing the effects of overlapping gene
sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
14 Hypergeometric Testing Used for Gene Set Enrichment
Analysis 207
S. Falcon and R. Gentleman
14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 207
14.2 The basic problem . . . . . . . . . . . . . . . . . . . . . . . 208
14.3 Preprocessing and inputs . . . . . . . . . . . . . . . . . . . 209
14.4 Outputs and result summarization . . . . . . . . . . . . . . 215
14.5 The conditional hypergeometric test . . . . . . . . . . . . . 218
14.6 Other collections of gene sets . . . . . . . . . . . . . . . . . 219
15 Solutions to Exercises 221
2 R and Bioconductor Introduction . . . . . . . . . . . . . . 221
3 Processing Affymetrix ExpressionData . . . . . . . . . . . 226
4 Two-Color Arrays . . . . . . . . . . . . . . . . . . . . . . . 230
5 Fold-Changes, Log-Ratios, Background Correction,
Shrinkage Estimation, and Variance Stabilization . . . . . 231
6 Easy Differential Expression . . . . . . . . . . . . . . . . . 233
7 Differential Expression. . . . . . . . . . . . . . . . . . . . . 233
8 Annotation andMetadata. . . . . . . . . . . . . . . . . . . 234
9 SupervisedMachine Learning . . . . . . . . . . . . . . . . . 241
10 UnsupervisedMachine Learning . . . . . . . . . . . . . . . 249
11 Using Graphs for Interactome Data . . . . . . . . . . . . . 256
12 Graph Layout . . . . . . . . . . . . . . . . . . . . . . . . . . 259
13 Gene Set Enrichment Analysis . . . . . . . . . . . . . . . . 261
14 Hypergeometric Testing Used for Gene Set Enrichment
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
References 271
Index 277


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