Bioconductor Case Studies / Edition 1

Bioconductor Case Studies / Edition 1

by Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon
     
 

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.

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Overview

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.

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Product Details

ISBN-13:
9780387772394
Publisher:
Springer New York
Publication date:
08/28/2008
Series:
Use R! Series
Edition description:
2008
Pages:
284
Product dimensions:
6.00(w) x 9.10(h) x 0.60(d)

Table of Contents

The ALL data set.- 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.- Solutions to exercises.- References.- Index.

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