Data Analysis Tools for DNA Microarrays (Book with CD-ROM)

by Draghici, Sorin

• ISBN13: 9781584883159
• ISBN10: 1584883154
• Edition: 1st
• Format: Hardcover
• Copyright: 2003-06-04
• Publisher: Chapman & Hall/

Summary

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

Introduction

Bioinformatics - an emerging discipline

The building blocks of genomic information

Expression of genetic information

The need for microarrays

Summary

Microarrays

Microarrays - tools for gene expression analysis

Fabrication of microarrays

Applications of microarrays

Challenges in using microarrays in gene expression studies

Sources of variability

Summary

Image processing

Introduction

Basic elements of digital imaging

Microarray image processing

Image processing of cDNA microarrays

Summary

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

Summary

Solved problems

Classical approaches to data analysis

Introduction

Tests involving a single sample

Tests involving two samples

Summary

Exercises

Analysis of Variance - ANOVA

Introduction

One-way ANOVA

Two-way ANOVA

Quality control

Summary

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

Summary

Multiple comparisons

Introduction

The problem of multiple comparisons

A more precise argument

Corrections for multiple comparisons

Summary

Analysis and visualization tools

Introduction

Box plots

Gene pies

Scatter plots

Histograms

Time Series

Principal component analysis (PCA)

Summary

Cluster analysis

Introduction

Metric distances

Clustering algorithms

Summary

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

Summary

Appendix

Methods for selecting differentially regulated genes

Introduction

Criteria

Fold change

Unusual ratio

Hypothesis testing, corrections for multiple comparisons and resampling

Description

Characteristics

ANOVA

Noise sampling

Model based maximum likelihood estimation methods

Affymetrix comparison calls

Other methods

Summary

Functional analysis and biological interpretation of microarray data

Introduction

The Gene Ontology

Other related resources

Mapping 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

Summary

Commercial applications

Introduction

Significance testing among groups using GeneSight

Statistical analysis of microarray data using S-PLUS and Insightful ArrayAnalyzer

SAS software for genomics

The road ahead

References