Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R.

This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms:

Curation and delivery of biological metadata for use in statistical modeling and interpretation

Statistical analysis of high-throughput data, including machine learning and visualization

Modeling and visualization of graphs and networks

The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies.

This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

Describes R-based packages for bioinformatics and computational biology (BCB) created in the Bioconductor project www.bioconductor.org Includes supplementary material: sn.pub/extras

Inhalt
Preprocessing data from genomic experiments.- Preprocessing Overview.- Preprocessing High-density Oligonucleotide Arrays.- Quality Assessment of Affymetrix GeneChip Data.- Preprocessing Two-Color Spotted Arrays.- Cell-Based Assays.- SELDI-TOF Mass Spectrometry Protein Data.- Meta-data: biological annotation and visualization.- Meta-data Resources and Tools in Bioconductor.- Querying On-line Resources.- Interactive Outputs.- Visualizing Data.- Statistical analysis for genomic experiments.- Analysis Overview.- Distance Measures in DNA Microarray Data Analysis.- Cluster Analysis of Genomic Data.- Analysis of Differential Gene Expression Studies.- Multiple Testing Procedures: the multtest Package and Applications to Genomics.- Machine Learning Concepts and Tools for Statistical Genomics.- Ensemble Methods of Computational Inference.- Browser-based Affymetrix Analysis and Annotation.- Graphs and networks.- and Motivating Examples.- Graphs.- Bioconductor Software for Graphs.- Case Studies Using Graphs on Biological Data.- Case studies.- limma: Linear Models for Microarray Data.- Classification with Gene Expression Data.- From CEL Files to Annotated Lists of Interesting Genes.