Bioconductor Case Studies

ISBN-10: 0387772391

ISBN-13: 9780387772394

Edition: 2008

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Book details

List price: $99.00
Copyright year: 2008
Publisher: Springer
Publication date: 8/15/2008
Binding: Paperback
Pages: 284
Size: 6.25" wide x 9.50" long x 0.75" tall
Weight: 1.320
Language: English

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