Statistics and Data Analysis for Microarrays Using R and Bioconductor

Statistics and Data Analysis for Microarrays Using R and Bioconductor

Hardback Chapman & Hall/CRC Mathematical & Computational Biology

By (author) Sorin Draghici

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  • Publisher: Chapman & Hall/CRC
  • Format: Hardback | 1036 pages
  • Dimensions: 160mm x 236mm x 48mm | 1,973g
  • Publication date: 11 January 2012
  • Publication City/Country: Boca Raton, FL
  • ISBN 10: 1439809755
  • ISBN 13: 9781439809754
  • Edition: 2, Revised
  • Edition statement: 2nd Revised edition
  • Illustrations note: 344 black & white illustrations, 24 black & white tables
  • Sales rank: 443,419

Product description

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.

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Author information

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.

Review quote

Praise for the First Edition The book by Draghici is an excellent choice to be used as a textbook for a graduate-level bioinformatics course. This well-written book with two accompanying CD-ROMs will create much-needed enthusiasm among statisticians. -Journal of Statistical Computation and Simulation, Vol. 74 I really like Draghici's book. As the author explains in the Preface, the book is intended to serve both the statistician who knows very little about DNA microarrays and the biologist who has no expertise in data analysis. The author lays out a study plan for the statistician that excludes 5 of the 17 chapters (4-8). These chapters present the basics of statistical distributions, estimation, hypothesis testing, ANOVA, and experimental design. What that leaves for the statistician is the three-chapter primer on microarrays and image processing, plus all of the data analysis tools specific to the microarray situation. ... it includes two CDs with trial versions of several specialised software packages. Anyone who uses microarray data should certainly own a copy. -Technometrics, Vol. 47, No. 1, February 2005

Table of contents

Introduction Bioinformatics - An Emerging Discipline The Cell and Its Basic Mechanisms The Cell The Building Blocks of Genomic Information Expression of Genetic Information The Need for High-Throughput Methods Microarrays Microarrays - Tools for Gene Expression Analysis Fabrication of Microarrays Applications of Microarrays Challenges in Using Microarrays in Gene Expression Studies Sources 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 Consistency Sources of Inaccuracy and Inconsistencies in Microarray Measurements The MicroArray Quality Control (MAQC) Project Image Processing Introduction Basic Elements of Digital Imaging Microarray Image Processing Image Processing of cDNA Microarrays Image Processing of Affymetrix Arrays Introduction to R Introduction to R The Basic Concepts Data Structures and Functions Other Capabilities The R Environment Installing Bioconductor Graphics Control Structures in R Programming in R vs C/C++/Java Bioconductor: Principles and Illustrations Overview The Portal Some Explorations and Analyses Elements of Statistics Introduction Some Basic Concepts Elementary Statistics Degrees of Freedom Probabilities Bayes' Theorem Testing for (or Predicting) a Disease Probability Distributions Probability Distributions Central Limit Theorem Are Replicates Useful? Basic Statistics in R Introduction Descriptive Statistics in R Probabilities and Distributions in R Central Limit Theorem Statistical Hypothesis Testing Introduction The Framework Hypothesis Testing and Significance "I Do Not Believe God Does Not Exist" An Algorithm for Hypothesis Testing Errors in Hypothesis Testing Classical Approaches to Data Analysis Introduction Tests Involving a Single Sample Tests Involving Two Samples Analysis of Variance (ANOVA) Introduction One-Way ANOVA Two-Way ANOVA Quality 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 Design The Concept of Experiment Design Comparing Varieties Improving the Production Process Principles of Experimental Design Guidelines for Experimental Design A Short Synthesis of Statistical Experiment Designs Some Microarray Specific Experiment Designs Multiple Comparisons Introduction The Problem of Multiple Comparisons A More Precise Argument Corrections for Multiple Comparisons Corrections for Multiple Comparisons in R Analysis and Visualization Tools Introduction Box Plots Gene Pies Scatter Plots Volcano Plots Histograms Time Series Time Series Plots in R Principal Component Analysis (PCA) Independent Component Analysis (ICA) Cluster Analysis Introduction Distance Metric Clustering Algorithms Partitioning around Medoids (PAM) Biclustering Clustering in R Quality Control Introduction Quality Control for Affymetrix Data Quality Control of Illumina Data Data Pre-Processing and Normalization Introduction General Pre-Processing Techniques Normalization Issues Specific to cDNA Data Normalization Issues Specific to Affymetrix Data Other Approaches to the Normalization of Affymetrix Data Useful Pre-Processing and Normalization Sequences Normalization Procedures in R Batch Pre-Processing Normalization Functions and Procedures for Illumina Data Methods for Selecting Differentially Regulated Genes Introduction Criteria Fold Change Unusual Ratio Hypothesis Testing, Corrections for Multiple Comparisons, and Resampling ANOVA Noise Sampling Model-Based Maximum Likelihood Estimation Methods Affymetrix Comparison Calls Significance 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 Data Over-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 Profiling Using 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 Tools Drawbacks and Limitations of the Current Approach Focused Microarrays - Comparison and Selection Introduction Criteria for Array Selection Onto-Compare Some Comparisons ID Mapping Issues Introduction Name 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 Analysis An Approach for the Analysis of Metabolic Pathways An Impact Analysis of Signaling Pathways Variations on the Impact Analysis Theme Pathway Guide Kinetic models vs. Impact Analysis Conclusions Data Sets and Software Availability Machine Learning Techniques Introduction Main Concepts and Definitions Supervised Learning Practicalities Using R The Road Ahead What Next? References A Summary appears at the end of each chapter.