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

DrÄ ghici, Sorin

ISBN 10: 1584883154 / ISBN 13: 9781584883159

Editore: Chapman and Hall/CRC, 2003

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Codice articolo rev5357790200

Dati bibliografici

• Titolo: Data Analysis Tools for DNA Microarrays (Chapman & Hall/CRC Mathematical and Computational Biology)
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2003
• Lingua: Inglese
• ISBN 10: 1584883154
• ISBN 13: 9781584883159
• Rilegatura: Rilegato
• Condizione: Very Good
• Peso: 884,5 grammi

Descrizione articolo

Riassunto

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 EditionCompletely 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 downloadable resource.

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.

Contenuti

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