Statistics and Data Analysis for Microarrays Using R and Bioconductor, 2nd Edition

Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems.

New to the Second Edition

Completely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying CD-ROM.

With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.

Table of Contents

Chapter 1 Introduction
Chapter 2 The cell and its basic mechanisms
Chapter 3 Microarrays
Chapter 4 Reliability and reproducibility issues in DNA microarray measurements
Chapter 5 Image processing
Chapter 6 Introduction to R
Chapter 7 Bioconductor: principles and illustrations
Chapter 8 Elements of statistics
Chapter 9 Probability distributions
Chapter 10 Basic statistics in R
Chapter 11 Statistical hypothesis testing
Chapter 12 Classical approaches to data analysis
Chapter 13 Analysis of Variance – ANOVA
Chapter 14 Linear models in R
Chapter 15 Experiment design
Chapter 16 Multiple comparisons
Chapter 17 Analysis and visualization tools
Chapter 18 Cluster analysis
Chapter 19 Quality control
Chapter 20 Data preprocessing and normalization
Chapter 21 Methods for selecting differentially expressed genes
Chapter 22 The Gene Ontology (GO)
Chapter 23 Functional analysis and biological interpretation of microarray data
Chapter 24 Uses, misuses, and abuses in GO profiling
Chapter 25 A comparison of several tools for ontological analysis
Chapter 26 Focused microarrays – comparison and selection
Chapter 27 ID Mapping issues
Chapter 28 Pathway analysis
Chapter 29 Machine learning techniques
Chapter 30 The road ahead