Bioconductor Case Studies
Seiten
2008
Springer-Verlag New York Inc.
978-0-387-77239-4 (ISBN)
Springer-Verlag New York Inc.
978-0-387-77239-4 (ISBN)
Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. (6) gene set enrichment analysis.
Each chapter of this book describes an analysis of real data using hands-on example driven approaches.
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.
Each chapter of this book describes an analysis of real data using hands-on example driven approaches.
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.
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.
Reihe/Serie | Use R! |
---|---|
Zusatzinfo | XII, 284 p. |
Verlagsort | New York, NY |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
Medizin / Pharmazie | |
Naturwissenschaften ► Biologie | |
ISBN-10 | 0-387-77239-1 / 0387772391 |
ISBN-13 | 978-0-387-77239-4 / 9780387772394 |
Zustand | Neuware |
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