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| Preface | |
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| List of Contributors | |
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| The ALL Dataset | |
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| Introduction | |
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| The ALL data | |
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| Data subsetting | |
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| Nonspecific filtering | |
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| BCR/ABL ALL1/AF4 subset | |
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| R and Bioconductor Introduction | |
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| Finding help in R | |
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| Working with packages | |
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| Some basic R | |
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| Structures for genomic data | |
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| Graphics | |
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| Processing Affymetrix Expression Data | |
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| The input data: CEL files | |
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| Quality assessment | |
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| Preprocessing | |
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| Ranking and filtering probe sets | |
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| Advanced preprocessing | |
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| Two-Color Arrays | |
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| Introduction | |
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| Data import | |
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| Image plots | |
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| Normalization | |
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| Differential expression | |
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| Fold-Changes, Log-Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization | |
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| Fold-changes and (log-)ratios | |
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| Background-correction and generalized logarithm | |
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| Calling VSN | |
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| How does VSN work? | |
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| Robust fitting and the "most genes not differentially expressed" assumption | |
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| Single-color normalization | |
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| The interpretation of glog-ratios | |
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| Reference normalization | |
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| Easy Differential Expression | |
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| Example data | |
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| Nonspecific filtering | |
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| Differential expression | |
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| Multiple testing correction | |
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| Differential Expression | |
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| Motivation | |
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| Nonspecific filtering | |
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| Differential expression | |
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| Multiple testing | |
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| Moderated test statistics and the limma package | |
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| Gene selection by Receiver Operator Characteristic (ROC) | |
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| When power increases | |
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| Annotation and Metadata | |
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| Our data | |
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| Multiple probe sets per gene | |
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| Categories and overrepresentation | |
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| Working with GO | |
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| Other annotations available | |
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| biomaRt | |
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| Database versions of annotation packages | |
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| Supervised Machine Learning | |
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| Introduction | |
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| The example dataset | |
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| Feature selection and standardization | |
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| Selecting a distance | |
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| Machine learning | |
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| Cross-validation | |
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| Random forests | |
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| Multigroup classification | |
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| Unsupervised Machine Learning | |
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| Preliminaries | |
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| Distances | |
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| How many clusters? | |
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| Hierarchical clustering | |
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| Partitioning methods | |
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| Self-organizing maps | |
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| Hopach | |
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| Silhouette plots | |
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| Exploring transformations | |
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| Remarks | |
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| Using Graphs for Interactome Data | |
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| Introduction | |
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| Exploring the protein interaction graph | |
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| The co-expression graph | |
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| Testing the association between physical interaction and coexpression | |
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| Some harder problems | |
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| Reading PSI-25 XML files from IntAct with the Rintact package | |
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| Graph Layout | |
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| Introduction | |
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| Layout and rendering using Rgraphviz | |
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| Directed graphs | |
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| Subgraphs | |
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| Tooltips and hyperlinks on graphs | |
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| Gene Set Enrichment Analysis | |
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| Introduction | |
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| Data analysis | |
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| Identifying and assessing the effects of overlapping gene sets | |
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| Hypergeometric Testing Used for Gene Set Enrichment Analysis | |
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| Introduction | |
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| The basic problem | |
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| Preprocessing and inputs | |
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| Outputs and result summarization | |
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| The conditional hypergeometric test | |
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| Other collections of gene sets | |
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| Solutions to Exercises | |
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| R and Bioconductor Introduction | |
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| Processing Affymetrix Expression Data | |
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| Two-Color Arrays | |
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| Fold-Changes, Log-Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization | |
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| Easy Differential Expression | |
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| Differential Expression | |
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| Annotation and Metadata | |
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| Supervised Machine Learning | |
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| Unsupervised Machine Learning | |
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| Using Graphs for Interactome Data | |
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| Graph Layout | |
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| Gene Set Enrichment Analysis | |
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| Hypergeometric Testing Used for Gene Set Enrichment Analysis | |
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| References | |
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| Index | |