From the reviews: "The book has several nice touches that readers will appreciate. First, the liberal use of color shows the full capabilities of Bioconductor pakages and brings the material to life. Second, color figures are dispersed throughout the text rather than being relegated to a central section of color plates. Third, the index indicates whether a term references a package, function or class. This book is an excellent resource... In summary, this book is a must have for any Bioconductor user." (J. Wade Davis, Journal of the American Statistical Association, Vol. 102, No. 477, 2007) "This book is solid evidence of the influence that quantitative researchers can have on biological investigations. Organized into separate chapters of shared authorship, the book provides a valuable overview of the impact that the authors and their colleagues have had on the analysis of genomic data." (R.W. Doerge, Biostatistics, December 2006) "This book provides an in-depth demonstration of the potential of the Bioconductor project, through a varied mixture of descriptions, figures and examples. ... The book ... is an exciting opportunity for researchers to learn directly from the software developers themselves. The range of material covered by the book is diverse and well structured. An abundance of fully worked case studies illustrate the methods in practice. ... it should be a must for any researcher considering getting started with the software ... ." (Rebecca Walls, Journal of Applied Statistics, Vol. 34 (3), 2007) "The book provides an extensive overview over the most important tasks in analyzing genomic data with Bioconductor. ... The book is well written and communicates hands-on experience of the developers of the respective Bioconductor packages themselves. ... The book is targeted to a broad range of researchers interested in genomic data analysis, including biologists, bioinformaticians, and statisticians. ... It is a very valuable resource for modern genomic data analysis. There is no comparable book on the market." (Jorg Rahnenfuhrer, Statistical Papers, Vol. 50, 2009)

• Preprocessing data from genomic experiments.- Preprocessing Overview.- Preprocessing High-density Oligonucleotide Arrays.- Quality Assessment of Affymetrix GeneChip Data.- Preprocessing Two-Color Spotted Arrays.- Cell-Based Assays.- SELDI-TOF Mass Spectrometry Protein Data.- Meta-data: biological annotation and visualization.- Meta-data Resources and Tools in Bioconductor.- Querying On-line Resources.- Interactive Outputs.- Visualizing Data.- Statistical analysis for genomic experiments.- Analysis Overview.- Distance Measures in DNA Microarray Data Analysis.- Cluster Analysis of Genomic Data.- Analysis of Differential Gene Expression Studies.- Multiple Testing Procedures: the multtest Package and Applications to Genomics.- Machine Learning Concepts and Tools for Statistical Genomics.- Ensemble Methods of Computational Inference.- Browser-based Affymetrix Analysis and Annotation.- Graphs and networks.- and Motivating Examples.- Graphs.- Bioconductor Software for Graphs.- Case Studies Using Graphs on Biological Data.- Case studies.- limma: Linear Models for Microarray Data.- Classification with Gene Expression Data.- From CEL Files to Annotated Lists of Interesting Genes.

From the reviews: "The book has several nice touches that readers will appreciate. First, the liberal use of color shows the full capabilities of Bioconductor pakages and brings the material to life. Second, color figures are dispersed throughout the text rather than being relegated to a central section of color plates. Third, the index indicates whether a term references a package, function or class. This book is an excellent resource... In summary, this book is a must have for any Bioconductor user." (J. Wade Davis, Journal of the American Statistical Association, Vol. 102, No. 477, 2007) "This book is solid evidence of the influence that quantitative researchers can have on biological investigations. Organized into separate chapters of shared authorship, the book provides a valuable overview of the impact that the authors and their colleagues have had on the analysis of genomic data." (R.W. Doerge, Biostatistics, December 2006) "This book provides an in-depth demonstration of the potential of the Bioconductor project, through a varied mixture of descriptions, figures and examples. ... The book ... is an exciting opportunity for researchers to learn directly from the software developers themselves. The range of material covered by the book is diverse and well structured. An abundance of fully worked case studies illustrate the methods in practice. ... it should be a must for any researcher considering getting started with the software ... ." (Rebecca Walls, Journal of Applied Statistics, Vol. 34 (3), 2007) "The book provides an extensive overview over the most important tasks in analyzing genomic data with Bioconductor. ... The book is well written and communicates hands-on experience of the developers of the respective Bioconductor packages themselves. ... The book is targeted to a broad range of researchers interested in genomic data analysis, including biologists, bioinformaticians, and statisticians. ... It is a very valuable resource for modern genomic data analysis. There is no comparable book on the market." (Jorg Rahnenfuhrer, Statistical Papers, Vol. 50, 2009)

• Preprocessing data from genomic experiments.- Preprocessing Overview.- Preprocessing High-density Oligonucleotide Arrays.- Quality Assessment of Affymetrix GeneChip Data.- Preprocessing Two-Color Spotted Arrays.- Cell-Based Assays.- SELDI-TOF Mass Spectrometry Protein Data.- Meta-data: biological annotation and visualization.- Meta-data Resources and Tools in Bioconductor.- Querying On-line Resources.- Interactive Outputs.- Visualizing Data.- Statistical analysis for genomic experiments.- Analysis Overview.- Distance Measures in DNA Microarray Data Analysis.- Cluster Analysis of Genomic Data.- Analysis of Differential Gene Expression Studies.- Multiple Testing Procedures: the multtest Package and Applications to Genomics.- Machine Learning Concepts and Tools for Statistical Genomics.- Ensemble Methods of Computational Inference.- Browser-based Affymetrix Analysis and Annotation.- Graphs and networks.- and Motivating Examples.- Graphs.- Bioconductor Software for Graphs.- Case Studies Using Graphs on Biological Data.- Case studies.- limma: Linear Models for Microarray Data.- Classification with Gene Expression Data.- From CEL Files to Annotated Lists of Interesting Genes.