Bioconductor Case Studies

Description

Preface -- Contents -- List of Contributors -- 1 The ALL Dataset -- 1.1 Introduction -- 1.2 The ALL data -- 1.3 Data subsetting -- 1.4 Nonspecific filtering -- 1.5 BCR/ABL ALL1/AF4 subset -- 2 R and BioconductorIntroduction -- 2.1 Finding help in R -- 2.2 Working with packages -- 2.3 Some basic R -- 2.3.1 Functions -- 2.3.2 The apply family of functions -- 2.3.3 Environments -- 2.4 Structures for genomic data -- 2.4.1 Building an ExpressionSet from .CEL and other files -- 2.4.2 Building an ExpressionSet from scratch -- 2.4.3 ExpressionSet basics -- 2.5 Graphics.;In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Each chapter describes an analysis of real data using hands-on example driven approaches. Short exercises are included. Preface -- Contents -- List of Contributors -- 1 The ALL Dataset -- 1.1 Introduction -- 1.2 The ALL data -- 1.3 Data subsetting -- 1.4 Nonspecific filtering -- 1.5 BCR/ABL ALL1/AF4 subset -- 2 R and BioconductorIntroduction -- 2.1 Finding help in R -- 2.2 Working with packages -- 2.3 Some basic R -- 2.3.1 Functions -- 2.3.2 The apply family of functions -- 2.3.3 Environments -- 2.4 Structures for genomic data -- 2.4.1 Building an ExpressionSet from .CEL and other files -- 2.4.2 Building an ExpressionSet from scratch -- 2.4.3 ExpressionSet basics -- 2.5 Graphics. 3 Processing AffymetrixExpression Data -- 3.1 The input data: CEL files -- 3.1.1 The sample annotation -- 3.2 Quality assessment -- 3.3 Preprocessing -- 3.4 Ranking and filtering probe sets -- 3.4.1 Summary statistics and tests for ranking -- 3.4.2 Visualization of differential expression -- 3.4.3 Highlighting interesting genes -- 3.4.4 Selecting hit lists and the multiple testing problem -- 3.4.5 Annotation -- 3.5 Advanced preprocessing -- 3.5.1 PM and MM probes -- 3.5.2 Background-correction -- 3.5.3 Summarization -- 4 Two-Color Arrays -- 4.1 Introduction -- 4.2 Data import -- 4.3 Image plots. 4.4 Normalization -- 4.5 Differential expression -- 5 Fold-Changes, Log-Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization -- 5.1 Fold-changes and (log- )ratios -- 5.2 Background-correction and generalized logarithm -- 5.3 Calling VSN -- 5.4 How does VSN work? -- 5.5 Robust fitting and the "most genes not differentially expressed" assumption -- 5.6 Single-color normalization -- 5.7 The interpretation of glog-ratios -- 5.8 Reference normalization -- 6 Easy Differential Expression -- 6.1 Example data -- 6.2 Nonspecific filtering -- 6.3 Differential expression. 6.4 Multiple testing correction -- 7 Differential Expression -- 7.1 Motivation -- 7.1.1 The gene-by-gene approach -- 7.1.2 Nonspecific filtering -- 7.1.3 Fold-change versus t-test -- 7.2 Nonspecific filtering -- 7.3 Differential expression -- 7.4 Multiple testing -- 7.5 Moderated test statistics and the limma package -- 7.5.1 Small sample sizes -- 7.6 Gene selection by Receiver Operator Characteristic (ROC) -- 7.7 When power increases -- 8 Annotation and Metadata -- 8.1 Our data -- 8.2 Multiple probe sets per gene -- 8.3 Categories and overrepresentation -- 8.3.1 Chromosomal location. 8.4 Working with GO -- 8.4.1 Functional analyses -- 8.5 Other annotations available -- 8.6 biomaRt -- 8.7 Database versions of annotation packages -- 8.7.1 Mapping Symbols -- 8.7.2 Other capabilities -- 9 Supervised Machine Learning -- 9.1 Introduction -- 9.1.1 Supervised machine learning check list -- 9.2 The example dataset -- 9.2.1 Nonspecific filtering of features -- 9.3 Feature selection and standardization -- 9.4 Selecting a distance -- 9.5 Machine learning -- 9.6 Cross-validation -- 9.7 Random forests -- 9.7.1 Feature selection -- 9.7.2 More exercises -- 9.8 Multigroup classification.