Bioinformatics and Computational Biology Solutions Using R and Bioconductor / Edition 1

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Full four-color book.

Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R.

All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies.

Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

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Editorial Reviews

From the Publisher
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." (Jörg Rahnenführer, Statistical Papers, Vol. 50, 2009)

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Product Details

  • ISBN-13: 9780387251462
  • Publisher: Springer New York
  • Publication date: 10/28/2005
  • Series: Statistics for Biology and Health Series
  • Edition description: 2005
  • Edition number: 1
  • Pages: 474
  • Product dimensions: 6.46 (w) x 9.56 (h) x 1.21 (d)

Table of Contents

Preprocessing overview –W. Huber, R. A. Irizarry, R. Gentleman.- Preprocessing High-density Oligonucleotide Arrays –B. M. Bolstad, R. A. Irizarry, L. Gautier, Z. Wu.- Quality Assessment of Affymetrix GeneChip Data –B. M. Bolstad, F. Collin, J. Brettschneider, K. Simpson, L. Cope, R. Irizarry, T. P. Speed.- Preprocessing Two-color Spotted Arrays –Y. H. Yang and A. C. Paquet.- Cell-based assays–W. Huber and F. Hahne.- SELDI-TOF Mass Spectrometry Protein Data –X. Li, R. Gentleman, X. Lu, Q. Shi, J.D. Iglehart, L. Harris and A. Miron.- Meta-data Resources and Tools in Bioconductor–R. Gentleman, V. J. Carey, and J. Zhang .- Querying on line resources –V. J. Carey, D. Temple Lang, J. Gentry, J. Zhang and R.Gentleman.- Interactive Outputs –C. A. Smith, W. Huber and R. Gentleman.- Visualizing Data–W.Huber, X. Li and R. Gentleman.- Analysis overview–V.J. Carey and R. Gentleman.- Distance Measures in DNA Microarray Data Analysis–R. Gentleman, B. Ding, S. Dudoit, and J. Ibrahim.- Cluster Analysis of Genomic Data –K. S. Pollard and M. J. van der Laan.- Analysis of differential gene expression studies–D. Scholtens and A. von Heydebreck.- Multiple Testing Procedures: R multtest Package and Applications to Genomics –K. S. Pollard, S. Dudoit, and M. J. van der Laan.- Machine learning concepts and tools for statistical genomics–V. J. Carey.- Ensemble methods of computational inference –T. Hothorn, M. Dettling, P. Bühlmann.- Browser-Based Affymetrix Analysis and Annotation –C. A. Smith.- Introduction and motivating examples–R. Gentleman, W. Huber and V. J. Carey.- Graphs–W. Huber, R. Gentleman and V. J. Carey.-Bioconductor software for graphs –V. J. Carey, R. Gentleman, W. Huber and J. Gentry.- Case Studies using Graphs on Biological Data–R. Gentleman, D. Scholtens, B. Ding, V. J. Carey, and W. Huber.- Limma: Linear Models for Microarray Data –G. K. Smyth.- Classification with Gene Expression Data –M. Dettling.- From Cel files to annotated lists of interesting genes –R. A. Irizarry

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