Buch, Englisch, 474 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 995 g
Buch, Englisch, 474 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 995 g
Reihe: Statistics for Biology and Health
ISBN: 978-0-387-25146-2
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Naturwissenschaften Biowissenschaften Tierkunde / Zoologie Tiergenetik, Reproduktion
Weitere Infos & Material
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.
