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Bioconductor Case Studies » (New Edition)

Book cover image of Bioconductor Case Studies by Florian Hahne

Authors: Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon
ISBN-13: 9780387772394, ISBN-10: 0387772391
Format: Paperback
Publisher: Springer-Verlag New York, LLC
Date Published: August 2008
Edition: New Edition

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Author Biography: Florian Hahne

Book Synopsis

Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include

* import and preprocessing of data from various sources

* statistical modeling of differential gene expression

* biological metadata

* application of graphs and graph rendering

* machine learning for clustering and classification problems

* gene set enrichment analysis

Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.

Table of Contents


Preface v List of Contributors xi
1 The ALL Dataset F. Hahne R. Gentleman 1
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 R. Gentleman F. Hahne S. Falcon M. Morgan 5
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 R. Gentleman W. Huber 25
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 Florian Hahne Wolfgang Huber 47
4.1 Introduction 47
4.2 Data import 48
4.3 Image plots 50
4.4 Normalization 50
4.5 Differential expression 57
5 Fold-Changes, Log-Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization W. Huber 63
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 F. Hahne W. Huber 83
6.1 Example data 83
6.2 Nonspecific filtering 84
6.3 Differential expression 85
6.4 Multiple testing correction 87
7 Differential Expression W. Huber D. Scholtens F. Hahne A. von Heydebreck 89
7.1 Motivation 89
7.2 Nonspecific filtering 90
7.3 Differential expression 92
7.4 Multiple testing 94
7.5 Moderated teststatistics 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 W. Huber F. Hahne 103
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
9 Supervised Machine Learning R. Gentleman W. Huber V. J. Carey 121
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 Random forests 132
9.8 Multigroup classification 135
10 Unsupervised Machine Learning R. Gentleman V. J. Carey 137
10.1 Preliminaries 137
10.2 Distances 139
10.3 How many clusters? 142
10.4 Hierarchical clustering 144
10.5 Partitioning methods 146
10.6 Self-organizing maps 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 T. Chiang S. Falcon F. Hahne W. Huber 159
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 F. Hahne W. Huber R. Gentleman 173
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
13 Gene Set Enrichment Analysis R. Gentleman M. Morgan W. Huber 193
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 S. Falcon R. Gentleman 207
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 Expression Data 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 and Metadata 234
9 Supervised Machine Learning 241
10 Unsupervised Machine 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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