Bioconductor case studies

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. Dynamic document: all computations and figures can be reproduced on a local computer Real data case studies and hands on exercises Companion website offering color figures and solutions to exercises INDICE: The ALL data set.- 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.- References.- Index.

• ISBN: 978-0-387-77239-4
• Editorial: Springer
• Encuadernacion: Rústica
• Páginas: 283
• Fecha Publicación: 01/06/2008
• Nº Volúmenes: 1
• Idioma: Inglés