| Description |
1 online resource (xlviii, 1042 pages) : illustrations (chiefly color. |
| Series |
Chapman & Hall/CRC mathematical and computational biology series |
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Chapman and Hall/CRC mathematical & computational biology series.
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| Summary |
"Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. New to the Second Edition Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data"-- Provided by publisher |
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"Preface Although the industry once suffered from a lack of qualified targets and candidate drugs, lead scientists must now decide where to start amidst the overload of biological data. In our opinion, this phenomenon has shifted the bottleneck in drug discovery from data collection to data anal- ysis, interpretation and integration. Life Science Informatics, UBS Warburg Market Report, 2001 One of the most promising tools available today to researchers in life sciences is the microarray technology. Typically, one DNA array will provide hundreds or thousands of gene expression values. However, the immense potential of this technology can only be realized if many such experiments are done. In order to understand the biological phenomena, expression levels need to be compared between species or between healthy and ill individuals or at different time points for the same individual or population of individuals. This approach is currently generating an immense quantity of data. Buried under this humongous pile of numbers lays invaluable biological information. The keys to understanding phenomena from fetal development to cancer may be found in these numbers. Clearly, powerful analysis techniques and algorithms are essential tools in mining these data. However, the computer scientist or statistician that does have the expertise to use advanced analysis techniques usually lacks the biological knowledge necessary to understand even the simplest biological phenomena. At the same time, the scientist having the right background to formulate and test biological hypotheses may feel a little uncomfortable when it comes to analyzing the data thus generated"-- Provided by publisher |
| Bibliography |
Includes bibliographical references (pages 981-1025) and index. |
| Contents |
Front Cover; Dedication; Contents; List of Figures; List of Tables; Preface; 1. Introduction; 2. The cell and its basic mechanisms; 3. Microarrays; 4. Reliability and reproducibility issues in DNA microarray measurements; 5. Image processing; 6. Introduction to R; 7. Bioconductor: principles and illustrations; 8. Elements of statistics; 9. Probability distributions; 10. Basic statistics in R; 11. Statistical hypothesis testing; 12. Classical approaches to data analysis; 13. Analysis of Variance -- ANOVA; 14. Linear models in R; 15. Experiment design; 16. Multiple comparisons. |
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17. Analysis and visualization tools18. Cluster analysis; 19. Quality control; 20. Data preprocessing and normalization ; 21. Methods for selecting differentially expressed genes; 22. The Gene Ontology (GO); 23. Functional analysis and biological interpretation of microarray data; 24. Uses, misuses, and abuses in GO profiling; 25. A comparison of several tools for ontological analysis; 26. Focused microarrays -- comparison and selection; 27. ID Mapping issues; 28. Pathway analysis; 29. Machine learning techniques; 30. The road ahead; Bibliography; Back Cover. |
| Subject |
Bioconductor (Computer file)
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Bioconductor (Computer file) |
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Bioconductor (Computer file) |
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DNA microarrays -- Statistical methods.
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Mathematical statistics -- Data processing.
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R (Computer program language)
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Programming languages (Electronic computers)
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Oligonucleotide Array Sequence Analysis |
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Data Interpretation, Statistical |
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Programming Languages |
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Puces à ADN -- Méthodes statistiques. |
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Statistique mathématique -- Informatique. |
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R (Langage de programmation) |
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Langages de programmation. |
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Programming languages (Electronic computers) |
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DNA microarrays -- Statistical methods |
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Mathematical statistics -- Data processing |
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R (Computer program language) |
| Other Form: |
Print version: Drăghici, Sorin. Statistics and data analysis for microarrays using R and Bioconductor. 2nd ed. Boca Raton, FL : CRC Press, ©2012 9781439809754 1439809755 (DLC) 2011036518 |
| ISBN |
1439809763 (electronic bk.) |
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9781439809761 (electronic bk.) |
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9780429130588 (ebook) |
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0429130589 |
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(hardback) |
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(hardback) |
| Standard No. |
3190231 |
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