Bioconductor Case Studies
Springer Science & Business Media, Jun 9, 2010 - Computers - 296 pages
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.
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3 Processing AffymetrixExpression Data
4 TwoColor Arrays
5 FoldChanges LogRatios Background Correction Shrinkage Estimation and Variance Stabilization
6 Easy Differential Expression
7 Differential Expression
8 Annotation and Metadata
9 Supervised Machine Learning
10 Unsupervised Machine Learning
Affymetrix algorithm analysis AnnotatedDataFrame annotation package arrays assess background-correction basic Bioconductor Bioconductor Case Studies CCl4 cell chapter color columns components compute correspond create cross-validation data.frame dataset default dendrogram differentially expressed genes distance distribution edges EntrezGene ID error rates estimate example Exercise expression data expression values ExpressionSet fold-change function function(x GC-content gene expression Gene Ontology gene sets GO terms graph object groups Hahne heatmap histogram Hypergeometric test identifiers incidence matrix intensities interaction interesting genes KEGG labels layout limma log-ratio log2 machine learning matrix metadata methods microarray mol.biol moltyp multiple testing names nodes nonspecific filtering normalization nsFilter number of genes obtained Ontology output p-values panel parameter pathway permutation PFAM plot probe sets protein random forest rendering Rgraphviz scatterplot selection shown in Figure Springer Science+Business Media statistics subgraphs subset t-statistic t-test TrainInd training set transcription factor variability vector