Buch, Englisch, 284 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 513 g
Reihe: Use R!
Buch, Englisch, 284 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 513 g
Reihe: Use R!
ISBN: 978-0-387-77239-4
Verlag: Springer
Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from various sources; (2) statistical modeling of differential gene expression; (3) biological metadata; (4) application of graphs and graph rendering; (5) machine learning for clustering and classification problems; (6) gene set enrichment analysis.
Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Mathematik | Informatik EDV | Informatik Informatik
- Naturwissenschaften Biowissenschaften Biowissenschaften
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
Weitere Infos & Material
The ALL Dataset.- R and Bioconductor Introduction.- Processing Affymetrix Expression Data.- Two Color Arrays.- Fold Changes, Log Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization.- Easy Differential Expression.- Differential Expression.- Annotation and Metadata.- Supervised Machine Learning.- Unsupervised Machine Learning.- Using Graphs for Interactome Data.- Graph Layout.- Gene Set Enrichment Analysis.- Hypergeometric Testing Used for Gene Set Enrichment Analysis.- Solutions to Exercises.
