Statistics and data analysis for microarrays using R and Bioconductor /

"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 formal...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Drăghici, Sorin (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press, ©2012.
Edición:2nd ed.
Colección:Chapman and Hall/CRC mathematical & computational biology series.
Temas:
Bioconductor (Computer file)
DNA microarrays > Statistical methods.
Mathematical statistics > Data processing.
R (Computer program language)
Programming languages (Electronic computers)
Oligonucleotide Array Sequence Analysis
Data Interpretation, Statistical
Programming Languages
Puces à ADN > Méthodes statistiques.
Statistique mathématique > Informatique.
R (Langage de programmation)
Langages de programmation.
SCIENCE > Biotechnology.
MATHEMATICS > Probability & Statistics > General.
SCIENCE > Life Sciences > Biology > General.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.
  • 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.

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