Data Analysis Tools for DNA Microarrays: (Chapman & Hall/CRC Mathematical and Computational Biology)

Technology today allows the collection of biological information at an unprecedented level of detail and in increasingly vast quantities. To reap real knowledge from the mountains of data produced, however, requires interdisciplinary skills-a background not only in biology but also in computer science and the tools and techniques of data analysis. To help meet the challenges of DNA research, Data Analysis Tools for DNA Microarrays builds the foundation in the statistics and data analysis tools needed by biologists and provides the overview of microarrays needed by computer scientists. It first presents the basics of microarray technology and more importantly, the specific problems the technology poses from the data analysis perspective. It then introduces the fundamentals of statistics and the details of the techniques most commonly used to analyze microarray data. The final chapter focuses on commercial applications with sections exploring various software packages from BioDiscovery, Insightful, SAS, and Spotfire. The book is richly illustrated with more than 230 figures in full color and comes with a CD-ROM containing full-feature trial versions of software for image analysis (ImaGene, BioDiscovery Inc.) and data analysis (GeneSight, BioDiscovery Inc. and S-Plus Array Analyzer, Insightful Inc.). Written in simple language and illustrated in full color, Data Analysis Tools for DNA Microarrays lowers the communication barrier between life scientists and analytical scientists. It prepares those charged with analyzing microarray data to make informed choices about the techniques to use in a given situation and contribute to further advances in the field.

Table of Contents:
PREFACE INTRODUCTION Bioinformatics - An Emerging Discipline The Building Blocks of Genomic Information Expression of Genetic Information The Need for Microarrays MICROARRAYS Microarrays - Tools for Gene Expression Analysis Fabrication of Microarrays Applications of Microarrays Challenges in Using Microarrays in Gene Expression Studies Sources of Variability IMAGE PROCESSING Introduction Basic Elements of Digital Imaging Microarray Image Processing Image Processing of cDNA Microarrays Image Processing of Affymetrix Microarrrays ELEMENTS OF STATISTICS Introduction Some Basic Terms Elementary Statistics Probabilities Bayes' Theorem Probability Distributions Central Limit Theorem Are Replicates Useful? Summary Solved Problems Exercises 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 Solved Problems CLASSICAL APPROACHES TO DATA ANALYSIS Introduction Tests Involving a Single Sample Tests Involving Two Samples Exercises ANALYSIS OF VARIANCE - ANOVA Introduction One-Way ANOVA Two-Way ANOVA Quality Control Exercises 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 ANALYSIS AND VISUALIZATION TOOLS Introduction Box Plots Gene Pies Scatter Plots Histograms Time Series Principal Component Analysis (PCA) Independent Component Analysis (ICA) CLUSTER ANALYSIS Introduction Metric Distances Hierarchical Clustering k-Means Clustering Kohonen Maps (SOFM) 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 Appendix 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 Other Methods Appendix FUNCTIONAL ANALYSIS AND BIOLOGICAL INTERPRETATION OF MICROARRAY DATA Introduction The Gene Ontology Other Related Resources Translating Lists of Differentially Regulated Genes into Biological Knowledge Onto-Express Summary FOCUSED MICROARRAYS - COMPARISON AND SELECTION Introduction Criteria for Array Selection Onto-Compare Some Comparisons COMMERCIAL APPLICATIONS Introduction Significance Testing Among Groups Using GeneSight Statistical Analysis of Microarray Data Using S-PLUS and Insightful ArrayAnalyzer SAS Software for Genomics Spofire's Decision Site THE ROAD AHEAD What Next? Molecular Diagnosis Gene Regulatory Networks Conclusions REFERENCES

Review :
"I really like Draghici's book. The biology and statistics are kept at a good level for the intended audience. Anyone who uses microarray data should certainly own a copy." - Technometrics, February 2005, Vol. 47, No. 1 "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. "The softcover book is reasonably priced, and it includes two CDs with trial versions of several specialised software packages. Anyone who uses microarray data should certainly own a copy." -Technometrics