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.
Table des matières
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.
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