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The Resource Bioinformatics and computational biology solutions using R and Bioconductor, editors, Robert Gentleman ... [et al.]

Bioinformatics and computational biology solutions using R and Bioconductor, editors, Robert Gentleman ... [et al.]

Label
Bioinformatics and computational biology solutions using R and Bioconductor
Title
Bioinformatics and computational biology solutions using R and Bioconductor
Statement of responsibility
editors, Robert Gentleman ... [et al.]
Contributor
Subject
Language
eng
Summary
"This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. 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."--BOOK JACKET
Cataloging source
UKM
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1959-
http://library.link/vocab/relatedWorkOrContributorName
Gentleman, Robert
Series statement
Statistics for biology and health
http://library.link/vocab/subjectName
  • R (Computer program language)
  • Bioinformatics
  • Computational biology
Target audience
specialized
Label
Bioinformatics and computational biology solutions using R and Bioconductor, editors, Robert Gentleman ... [et al.]
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. [445]-463) and index
Contents
  • I.
  • Preprocessing data from genomic experiments.
  • p. 1
  • p. 3
  • 1.
  • Preprocessing overview/
  • W. Huber
  • R. A. Irizarry
  • R. Gentleman
  • Z. Wu
  • p. 13
  • 2.
  • Preprocessing high-density oligonucleotide arrays/
  • B. M. Bolstad
  • R. A. Irizarry
  • L. Gautier
  • K. Simpson
  • L. Cope
  • R. A. Irizarry
  • T. P. Speed
  • p. 33
  • 3.
  • Quality assessment of affymetrix GeneChip data/
  • B. M. Bolstad
  • F. Collin
  • J. Brettschneider
  • 4.
  • Preprocessing two-color spotted arrays/
  • Y. H. Yang
  • A. C. Paquet
  • p. 49
  • 5.
  • Cell-based assays/
  • W. Huber
  • F. Hahne
  • p. 71
  • Q. Shi
  • J. D. Iglehart
  • L. Harris
  • A. Miron
  • p. 91
  • 6.
  • SELDI-TOF mass spectrometry protein data/
  • X. Li
  • R. Gentleman
  • X. Lu
  • II.
  • Meta-data : biological annotation and visualization.
  • p. 111
  • p. 113
  • 7.
  • Meta-data resources and tools in bioconductor/
  • R. Gentleman
  • V. J. Carey
  • J. Zhang
  • J. Zhang
  • R. Gentleman
  • p. 135
  • 8.
  • Querying on-line resources/
  • V. J. Carey
  • D. Temple Lang
  • J. Gentry
  • p. 147
  • 9.
  • Interactive outputs/
  • C. A. Smith
  • W. Huber
  • R. Gentleman
  • p. 161
  • 10.
  • Visualizing data/
  • W. Huber
  • X. Li
  • R. Gentleman
  • III.
  • Statistical analysis for genomic experiments.
  • p. 181
  • 11.
  • Analysis overview/
  • V. J. Carey
  • R. Gentleman
  • p. 183
  • J. Ibrahmim
  • p. 189
  • 12.
  • Distance measures in DNA microarray data analysis/
  • R. Gentleman
  • B. Ding
  • S. Dudoit
  • 13.
  • Cluster analysis of genomic data/
  • K. S. Pollard
  • M. J. van der Laan
  • p. 209
  • 14.
  • Analysis of differential gene expression studies/
  • D. Scholtens
  • A. von Heydebreck
  • p. 229
  • p. 249
  • 15.
  • Multiple testing procedures : the multtest package and applications to genomics/
  • K. S. Pollard
  • S. Dudoit
  • M. J. van der Laan
  • 16.
  • Machine learning concepts and tools for statistical genomics/
  • V. J. Carey
  • p. 273
  • p. 293
  • 17.
  • Ensemble methods of computational inference/
  • T. Hothorn
  • M. Dettling
  • P. Buhlmann
  • 18.
  • Browser-based affymetrix analysis and annotation/
  • C. A. Smith
  • p. 313
  • IV.
  • Graphs and networks.
  • p. 327
  • p. 329
  • 19.
  • Introduction and motivating examples/
  • R. Gentleman
  • W. Huber
  • V. J. Carey
  • p. 337
  • 20.
  • Graphs/
  • W. Huber
  • R. Gentleman
  • V. J. Carey
  • J. Gentry
  • p. 347
  • 21.
  • Bioconductor software for graphs/
  • V. J. Carey
  • R. Gentleman
  • W. Huber
  • V. J. Carey
  • W. Huber
  • p. 369
  • 22.
  • Case studies using graphs on biological data/
  • R. Gentleman
  • D. Scholtens
  • B. Ding
  • V.
  • Case studies.
  • p. 395
  • 23.
  • Limma : linear models for microarray data/
  • G. K. Smyth
  • p. 397
  • 24.
  • Classification with gene expression data/
  • M. Dettling
  • p. 421
  • 25.
  • From CEL files to annotated lists of interesting genes/
  • R. A. Irizarry
  • p. 431
Dimensions
25 cm
Extent
xix, 473 p.
Isbn
9780387251462
Lccn
2005923843
Other physical details
ill.
Label
Bioinformatics and computational biology solutions using R and Bioconductor, editors, Robert Gentleman ... [et al.]
Publication
Bibliography note
Includes bibliographical references (p. [445]-463) and index
Contents
  • I.
  • Preprocessing data from genomic experiments.
  • p. 1
  • p. 3
  • 1.
  • Preprocessing overview/
  • W. Huber
  • R. A. Irizarry
  • R. Gentleman
  • Z. Wu
  • p. 13
  • 2.
  • Preprocessing high-density oligonucleotide arrays/
  • B. M. Bolstad
  • R. A. Irizarry
  • L. Gautier
  • K. Simpson
  • L. Cope
  • R. A. Irizarry
  • T. P. Speed
  • p. 33
  • 3.
  • Quality assessment of affymetrix GeneChip data/
  • B. M. Bolstad
  • F. Collin
  • J. Brettschneider
  • 4.
  • Preprocessing two-color spotted arrays/
  • Y. H. Yang
  • A. C. Paquet
  • p. 49
  • 5.
  • Cell-based assays/
  • W. Huber
  • F. Hahne
  • p. 71
  • Q. Shi
  • J. D. Iglehart
  • L. Harris
  • A. Miron
  • p. 91
  • 6.
  • SELDI-TOF mass spectrometry protein data/
  • X. Li
  • R. Gentleman
  • X. Lu
  • II.
  • Meta-data : biological annotation and visualization.
  • p. 111
  • p. 113
  • 7.
  • Meta-data resources and tools in bioconductor/
  • R. Gentleman
  • V. J. Carey
  • J. Zhang
  • J. Zhang
  • R. Gentleman
  • p. 135
  • 8.
  • Querying on-line resources/
  • V. J. Carey
  • D. Temple Lang
  • J. Gentry
  • p. 147
  • 9.
  • Interactive outputs/
  • C. A. Smith
  • W. Huber
  • R. Gentleman
  • p. 161
  • 10.
  • Visualizing data/
  • W. Huber
  • X. Li
  • R. Gentleman
  • III.
  • Statistical analysis for genomic experiments.
  • p. 181
  • 11.
  • Analysis overview/
  • V. J. Carey
  • R. Gentleman
  • p. 183
  • J. Ibrahmim
  • p. 189
  • 12.
  • Distance measures in DNA microarray data analysis/
  • R. Gentleman
  • B. Ding
  • S. Dudoit
  • 13.
  • Cluster analysis of genomic data/
  • K. S. Pollard
  • M. J. van der Laan
  • p. 209
  • 14.
  • Analysis of differential gene expression studies/
  • D. Scholtens
  • A. von Heydebreck
  • p. 229
  • p. 249
  • 15.
  • Multiple testing procedures : the multtest package and applications to genomics/
  • K. S. Pollard
  • S. Dudoit
  • M. J. van der Laan
  • 16.
  • Machine learning concepts and tools for statistical genomics/
  • V. J. Carey
  • p. 273
  • p. 293
  • 17.
  • Ensemble methods of computational inference/
  • T. Hothorn
  • M. Dettling
  • P. Buhlmann
  • 18.
  • Browser-based affymetrix analysis and annotation/
  • C. A. Smith
  • p. 313
  • IV.
  • Graphs and networks.
  • p. 327
  • p. 329
  • 19.
  • Introduction and motivating examples/
  • R. Gentleman
  • W. Huber
  • V. J. Carey
  • p. 337
  • 20.
  • Graphs/
  • W. Huber
  • R. Gentleman
  • V. J. Carey
  • J. Gentry
  • p. 347
  • 21.
  • Bioconductor software for graphs/
  • V. J. Carey
  • R. Gentleman
  • W. Huber
  • V. J. Carey
  • W. Huber
  • p. 369
  • 22.
  • Case studies using graphs on biological data/
  • R. Gentleman
  • D. Scholtens
  • B. Ding
  • V.
  • Case studies.
  • p. 395
  • 23.
  • Limma : linear models for microarray data/
  • G. K. Smyth
  • p. 397
  • 24.
  • Classification with gene expression data/
  • M. Dettling
  • p. 421
  • 25.
  • From CEL files to annotated lists of interesting genes/
  • R. A. Irizarry
  • p. 431
Dimensions
25 cm
Extent
xix, 473 p.
Isbn
9780387251462
Lccn
2005923843
Other physical details
ill.

Library Locations

    • Harold Cohen LibraryBorrow it
      Ashton Street, Liverpool, L69 3DA, GB
      53.418074 -2.967913
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