A Practical Approach to Microarray Data Analysis

In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships. Introduction to Microarray Data Analysis....Pages 1-46 Data Pre-Processing Issues in Microarray Analysis....Pages 47-64 Missing Value Estimation....Pages 65-75 Normalization....Pages 76-90 Singular Value Decomposition and Principal Component Analysis....Pages 91-109 Feature Selection in Microarray Analysis....Pages 110-131 Introduction to Classification in Microarray Experiments....Pages 132-149 Bayesian Network Classifiers for Gene Expression Analysis....Pages 150-165 Classifying Microarray Data Using Support Vector Machines....Pages 166-185 Weighted Flexible Compound Covariate Method for Classifying Microarray Data....Pages 186-200 Classification of Expression Patterns Using Artificial Neural Networks....Pages 201-215 Gene Selection and Sample Classification Using a Genetic Algorithm and k -Nearest Neighbor Method....Pages 216-229 Clustering Genomic Expression Data: Design and Evaluation Principles....Pages 230-245 Clustering or Automatic Class Discovery: Hierarchical Methods....Pages 246-260 Discovering Genomic Expression Patterns with Self-Organizing Neural Networks....Pages 261-273 Clustering or Automatic Class Discovery: Non-Hierarchical, non-SOM....Pages 274-288 Correlation and Association Analysis....Pages 289-305 Global Functional Profiling of Gene Expression Data....Pages 306-325 Microarray Software Review....Pages 326-344 Microarray Analysis as a Process....Pages 345-360

A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science.
Key topics covered include:

-Format of result from data analysis, analytical modeling/experimentation;
-Validation of analytical results;
-Data analysis/Modeling task;
-Analysis/modeling tools;
-Scientific questions, goals, and tasks;
-Application;
-Data analysis methods;
-Criteria for assessing analysis methodologies, models, and tools.