Wang / Petronis DNA Methylation Microarrays

Experimental Design and Statistical Analysis
1. Auflage 2008
ISBN: 978-1-4200-6728-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 256 Seiten

Reihe: Chapman & Hall/CRC Biostatistics Series

Providing an interface between dry-bench bioinformaticians and wet-lab biologists, DNA Methylation Microarrays: Experimental Design and Statistical Analysis presents the statistical methods and tools to analyze high-throughput epigenomic data, in particular, DNA methylation microarray data. Since these microarrays share the same underlying principles as gene expression microarrays, many of the analyses in the text also apply to microarray-based gene expression and histone modification (ChIP-on-chip) studies. After introducing basic statistics, the book describes wet-bench technologies that produce the data for analysis and explains how to preprocess the data to remove systematic artifacts resulting from measurement imperfections. It then explores differential methylation and genomic tiling arrays. Focusing on exploratory data analysis, the next several chapters show how cluster and network analyses can link the functions and roles of unannotated DNA elements with known ones. The book concludes by surveying the open source software (R and Bioconductor), public databases, and other online resources available for microarray research. Requiring only limited knowledge of statistics and programming, this book helps readers gain a solid understanding of the methodological foundations of DNA microarray analysis.

Zielgruppe

Ph.D. students, postdocs, and researchers using microarrays.

Autoren/Hrsg.

Wang, Sun-Chong

Petronis, Art

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften DNA und Transgene Organismen

Inhaltsverzeichnis

Preface

Applied Statistics

Descriptive statistics

Inferential statistics

DNA Methylation Microarrays and Quality Control

DNA methylation microarrays

Workflow of methylome experiment

Image analysis

Visualization of raw data

Reproducibility

Experimental Design

Goals of experiment

Reference design

Balanced block design

Loop design

Factorial design

Time course experimental design

How many samples/arrays are needed?

Appendix

Data Normalization

Measure of methylation

The need for normalization

Strategy for normalization

Two-color CpG island microarray normalization

Oligonucleotide arrays normalization

Normalization using control sequences

Appendix

Significant Differential Methylation

Fold change

Linear model for log-ratios or log-intensities

t test for contrasts

F test for joint contrasts

P-value adjustment for multiple testing

Modified t and F tests

Significant variation within and between groups

Significant correlation with a covariate

Permutation test for bisulfite sequence data

Missing data values

Appendix

High-Density Genomic Tiling Arrays

Normalization

Wilcoxon test in a sliding window

Boundaries of methylation regions

Multiscale analysis by wavelets

Unsupervised segmentation by hidden Markov model

Principal component analysis and biplot

Cluster Analysis

Measure of dissimilarity

Dimensionality reduction

Hierarchical clustering

K-means clustering

Model-based clustering

Quality of clustering

Statistical significance of clusters

Reproducibility of clusters

Repeated measurements

Statistical Classification

Feature selection

Discriminant function

K-nearest neighbor

Performance assessment

Interdependency Network of DNA Methylation

Graphs and networks

Partial correlation

Dependence networks from DNA methylation microarrays

Network analysis

Time Series Experiment

Regulatory networks from microarray data

Dynamic model of regulation

A penalized likelihood score for parsimonious model

Optimization by genetic algorithms

Online Annotations

Gene centric resources

PubMeth: A cancer methylation database

Gene Ontology

Kyoto Encyclopedia of Genes and Genomes

UniProt/Swiss-Prot protein knowledgebase

The International HapMap Project

UCSC human genome browser

Public Microarray Data Repositories

Epigenetics Society

Microarray Gene Expression Data Society

Minimum Information About a Microarray Experiment

Public repositories for high-throughput arrays

Open Source Software for Microarray Data Analysis

R: A language and environment for statistical computing and graphics

Bioconductor

References

Index