Statistics and Data Analysis for Microarrays Using R and Bioconductor

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 EditionCompletely 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 downloadable resource.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.

IntroductionBioinformatics — An Emerging Discipline The Cell and Its Basic MechanismsThe CellThe Building Blocks of Genomic InformationExpression of Genetic InformationThe Need for High-Throughput Methods MicroarraysMicroarrays — Tools for Gene Expression AnalysisFabrication of MicroarraysApplications of MicroarraysChallenges in Using Microarrays in Gene Expression StudiesSources of Variability Reliability and Reproducibility Issues in DNA Microarray Measurements Introduction What Is Expected from Microarrays? Basic Considerations of Microarray Measurements Sensitivity Accuracy Reproducibility Cross Platform ConsistencySources of Inaccuracy and Inconsistencies in Microarray MeasurementsThe MicroArray Quality Control (MAQC) Project Image ProcessingIntroductionBasic Elements of Digital ImagingMicroarray Image ProcessingImage Processing of cDNA MicroarraysImage Processing of Affymetrix Arrays Introduction to R Introduction to RThe Basic ConceptsData Structures and FunctionsOther CapabilitiesThe R EnvironmentInstalling Bioconductor Graphics Control Structures in RProgramming in R vs C/C++/Java Bioconductor: Principles and Illustrations Overview The Portal Some Explorations and Analyses Elements of StatisticsIntroductionSome Basic ConceptsElementary StatisticsDegrees of FreedomProbabilitiesBayes’ TheoremTesting for (or Predicting) a Disease Probability Distributions Probability DistributionsCentral Limit TheoremAre Replicates Useful? Basic Statistics in R Introduction Descriptive Statistics in RProbabilities and Distributions in RCentral Limit Theorem Statistical Hypothesis TestingIntroductionThe FrameworkHypothesis Testing and Significance"I Do Not Believe God Does Not Exist"An Algorithm for Hypothesis TestingErrors in Hypothesis Testing Classical Approaches to Data AnalysisIntroductionTests Involving a Single SampleTests Involving Two Samples Analysis of Variance (ANOVA)IntroductionOne-Way ANOVATwo-Way ANOVAQuality Control Linear Models in R Introduction and Model Formulation Fitting Linear Models in R Extracting Information from a Fitted Model: Testing Hypotheses and Making Predictions Some Limitations of the Linear Models Dealing with Multiple Predictors and Interactions in the Linear Models, and Interpreting Model Coefficients Experiment DesignThe Concept of Experiment DesignComparing VarietiesImproving the Production ProcessPrinciples of Experimental DesignGuidelines for Experimental DesignA Short Synthesis of Statistical Experiment DesignsSome Microarray Specific Experiment Designs Multiple ComparisonsIntroductionThe Problem of Multiple ComparisonsA More Precise ArgumentCorrections for Multiple ComparisonsCorrections for Multiple Comparisons in R Analysis and Visualization ToolsIntroductionBox PlotsGene PiesScatter PlotsVolcano PlotsHistogramsTime SeriesTime Series Plots in RPrincipal Component Analysis (PCA)Independent Component Analysis (ICA) Cluster AnalysisIntroductionDistance MetricClustering AlgorithmsPartitioning around Medoids (PAM) BiclusteringClustering in R Quality Control Introduction Quality Control for Affymetrix DataQuality Control of Illumina Data Data Pre-Processing and NormalizationIntroductionGeneral Pre-Processing TechniquesNormalization Issues Specific to cDNA DataNormalization Issues Specific to Affymetrix DataOther Approaches to the Normalization of Affymetrix DataUseful Pre-Processing and Normalization SequencesNormalization Procedures in RBatch Pre-Processing Normalization Functions and Procedures for Illumina Data Methods for Selecting Differentially Regulated GenesIntroductionCriteriaFold ChangeUnusual RatioHypothesis Testing, Corrections for Multiple Comparisons, and ResamplingANOVANoise SamplingModel-Based Maximum Likelihood Estimation MethodsAffymetrix Comparison CallsSignificance Analysis of Microarrays (SAM) A Moderated t-Statistic Other Methods Reproducibility Selecting Differentially Expressed (DE) Genes in R The Gene Ontology (GO) Introduction The Need for an Ontology What Is the Gene Ontology (GO)? What Does GO Contain?Access to GO Other Related Resources Functional Analysis and Biological Interpretation of Microarray DataOver-Representation Analysis (ORA)Onto-Express Functional Class Scoring The Gene Set Enrichment Analysis (GSEA) Uses, Misuses, and Abuses in GO Profiling Introduction "Known Unknowns" Which Way Is Up? Negative Annotations Common Mistakes in Functional ProfilingUsing a Custom Level of Abstraction through the GO Hierarchy Correlation between GO Terms GO Slims and Subsets A Comparison of Several Tools for Ontological Analysis Introduction Existing tools for Ontological Analysis Comparison of Existing Functional Profiling ToolsDrawbacks and Limitations of the Current Approach Focused Microarrays — Comparison and SelectionIntroduction Criteria for Array Selection Onto-Compare Some Comparisons ID Mapping Issues IntroductionName Space Issues in Annotation Databases A Comparison of Some ID Mapping Tools Pathway Analysis Terms and Problem Definition Over-Representation and Functional Class Scoring Approaches in Pathway AnalysisAn Approach for the Analysis of Metabolic Pathways An Impact Analysis of Signaling PathwaysVariations on the Impact Analysis ThemePathway GuideKinetic models vs. Impact Analysis Conclusions Data Sets and Software Availability Machine Learning Techniques Introduction Main Concepts and Definitions Supervised LearningPracticalities Using R The Road Ahead What Next? References A Summary appears at the end of each chapter.

Sorin Draghici the Robert J. Sokol MD Endowed Chair in Systems Biology in the Department of Obstetrics and Gynecology, professor in the Department of Clinical and Translational Science and Department of Computer Science, and head of the Intelligent Systems and Bioinformatics Laboratory at Wayne State University. He is also the chief of the Bioinformatics and Data Analysis Section in the Perinatology Research Branch of the National Institute for Child Health and Development. A senior member of IEEE, Dr. Draghici is an editor of IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal of Biomedicine and Biotechnology, and International Journal of Functional Informatics and Personalized Medicine. He earned a Ph.D. in computer science from the University of St. Andrews.