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