Ebook: The Analysis of Gene Expression Data: Methods and Software

Thedevelopmentoftechnologiesforhigh–throughputmeasurementofgene expression in biological system is providing powerful new tools for inv- tigating the transcriptome on a genomic scale, and across diverse biol- ical systems and experimental designs. This technological transformation is generating an increasing demand for data analysis in biological inv- tigations of gene expression. This book focuses on data analysis of gene expression microarrays. The goal is to provide guidance to practitioners in deciding which statistical approaches and packages may be indicated for their projects, in choosing among the various options provided by those packages, and in correctly interpreting the results. The book is a collection of chapters written by authors of statistical so- ware for microarray data analysis. Each chapter describes the conceptual and methodological underpinning of data analysis tools as well as their software implementation, and will enable readers to both understand and implement an analysis approach. Methods touch on all aspects of statis- cal analysis of microarrays, from annotation and ?ltering to clustering and classi?cation. All software packages described are free to academic users. The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion and are designed to be accessible to microarray data analystswithoutformalquantitativetraining.Mostchaptersaredirectedat microarray data analysts with master’s-level training in computer science, biostatistics, or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.

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This book presents practical approaches for the analysis of data from gene expression microarrays. Each chapter describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. Methods cover all aspects of statistical analysis of microarrays, from annotation and filtering to clustering and classification. Chapters are written by the developers of the software. All software packages described are free to academic users. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools.

The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion, and are designed to be accessible to microarray data analysts without formal quantitative training. Most chapters are directed at microarray data analysts with master-level training in computer science, biostatistics or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.

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This book presents practical approaches for the analysis of data from gene expression microarrays. Each chapter describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. Methods cover all aspects of statistical analysis of microarrays, from annotation and filtering to clustering and classification. Chapters are written by the developers of the software. All software packages described are free to academic users. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools.

The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion, and are designed to be accessible to microarray data analysts without formal quantitative training. Most chapters are directed at microarray data analysts with master-level training in computer science, biostatistics or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.

Content:
Front Matter....Pages i-xix
The Analysis of Gene Expression Data: An Overview of Methods and Software....Pages 1-45
Visualization and Annotation of Genomic Experiments....Pages 46-72
Bioconductor R Packages for Exploratory Analysis and Normalization of cDNA Microarray Data....Pages 73-101
An R Package for Analyses of Affymetrix Oligonucleotide Arrays....Pages 102-119
DNA-Chip Analyzer (dChip)....Pages 120-141
Expression Profiler....Pages 142-162
An S-PLUS Library for the Analysis and Visualization of Differential Expression....Pages 163-184
Dragon and Dragon View: Methods for the Annotation, Analysis, and Visualization of Large-Scale Gene Expression Data....Pages 185-209
Snomad: Biologist-Friendly Web Tools for the Standardization and NOrmalization of Microarray Data....Pages 210-228
Microarray Analysis Using the MicroArray Explorer....Pages 229-253
Parametric Empirical Bayes Methods for Microarrays....Pages 254-271
SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays....Pages 272-290
Adaptive Gene Picking with Microarray Data: Detecting Important Low Abundance Signals....Pages 291-312
MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments....Pages 313-341
GeneClust....Pages 342-361
POE: Statistical Methods for Qualitative Analysis of Gene Expression....Pages 362-387
Bayesian Decomposition....Pages 388-408
Bayesian Clustering of Gene Expression Dynamics....Pages 409-427
Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data....Pages 428-446
Back Matter....Pages 447-455

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This book presents practical approaches for the analysis of data from gene expression microarrays. Each chapter describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. Methods cover all aspects of statistical analysis of microarrays, from annotation and filtering to clustering and classification. Chapters are written by the developers of the software. All software packages described are free to academic users. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools.

The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion, and are designed to be accessible to microarray data analysts without formal quantitative training. Most chapters are directed at microarray data analysts with master-level training in computer science, biostatistics or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.

Content:
Front Matter....Pages i-xix
The Analysis of Gene Expression Data: An Overview of Methods and Software....Pages 1-45
Visualization and Annotation of Genomic Experiments....Pages 46-72
Bioconductor R Packages for Exploratory Analysis and Normalization of cDNA Microarray Data....Pages 73-101
An R Package for Analyses of Affymetrix Oligonucleotide Arrays....Pages 102-119
DNA-Chip Analyzer (dChip)....Pages 120-141
Expression Profiler....Pages 142-162
An S-PLUS Library for the Analysis and Visualization of Differential Expression....Pages 163-184
Dragon and Dragon View: Methods for the Annotation, Analysis, and Visualization of Large-Scale Gene Expression Data....Pages 185-209
Snomad: Biologist-Friendly Web Tools for the Standardization and NOrmalization of Microarray Data....Pages 210-228
Microarray Analysis Using the MicroArray Explorer....Pages 229-253
Parametric Empirical Bayes Methods for Microarrays....Pages 254-271
SAM Thresholding and False Discovery Rates for Detecting Differential Gene Expression in DNA Microarrays....Pages 272-290
Adaptive Gene Picking with Microarray Data: Detecting Important Low Abundance Signals....Pages 291-312
MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments....Pages 313-341
GeneClust....Pages 342-361
POE: Statistical Methods for Qualitative Analysis of Gene Expression....Pages 362-387
Bayesian Decomposition....Pages 388-408
Bayesian Clustering of Gene Expression Dynamics....Pages 409-427
Relevance Networks: A First Step Toward Finding Genetic Regulatory Networks Within Microarray Data....Pages 428-446
Back Matter....Pages 447-455
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