DNA Methylation Microarrays: Experimental Design and Statistical Analysis, 1st Edition (Hardback) book cover

DNA Methylation Microarrays

Experimental Design and Statistical Analysis, 1st Edition

By Sun-Chong Wang, Art Petronis

Chapman and Hall/CRC

256 pages | 102 B/W Illus.

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Hardback: 9781420067279
pub: 2008-04-24
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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.


I found the book to be very informative and a timely introduction to the issues related to designing and analyzing array-based methylation experiments. … it provides a solid grounding and serves as a good reference book for any statistician venturing into this field.

—Sarah Bujac, Pharmaceutical Statistics, 2011, 10

…a useful presentation of four detailed, well-written parts concerning techniques in the analysis of high throughput epigenomic data … a consistent and self-contained overview on important fundamental and modern procedures used by researchers in biology, bioinformatics, experimental designs …The book is of great interest to research workers who use the above-mentioned procedures in experimental design and deep analysis of epigenomic data with sound statistics.

—Cryssoula Ganatsiou, Zentralblatt MATH 1172

…This book is a helpful guide for researchers and students with an interest in performing genomic studies using high-throughput microarrays. … A wide range of useful data analysis tools are covered … Other strengths throughout the book include the discussion of experimental design, the mention of software for certain analyses, and the inclusion of more advanced methods such as wavelets and genetic algorithms. … Overall, this book gives a nice summary of methods used for the analysis of hybridization-based microarray data. …

Biometrics, March 2009

Table of Contents


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


Experimental Design

Goals of experiment

Reference design

Balanced block design

Loop design

Factorial design

Time course experimental design

How many samples/arrays are needed?


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


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


High-Density Genomic Tiling Arrays


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




About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
SCIENCE / Life Sciences / Biology / General
SCIENCE / Life Sciences / Biology / Molecular Biology