DNA Methylation Microarrays

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A01=Art Petronis
A01=Sun-Chong Wang
advanced DNA methylation data analysis
Author_Art Petronis
Author_Sun-Chong Wang
Balanced Block Design
biostatistics methods
Category=PSD
cluster analysis techniques
cpg
CpG Island
differential
Differential Methylation
DNA Methylation
DNA Methylation Fragment
DNA Methylation Microarrays
DNA Sequence Variation
DNAmethylation
epigenetic data analysis
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
experiments
histone
Hybridization Intensity
hypothesis
island
Linear Discriminant Analysis
Log Ratio
log2
Methylation Profiling
Methylation Sensitive Restriction Enzymes
Microarray Data
Microarray Experiment
modification
network biology
Null Hypothesis
Oligonucleotide Chips
Open Source Software
profile
Profile DNA Methylation
Promote DNA Methylation
public genomic databases
R programming for genomics
Roc Curve
Silhouette Width
Technical Variability
Tiling Arrays
Wilcoxon Signed Rank Test

Product details

  • ISBN 9780367387402
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Oct 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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.

Wang, Sun-Chong; Petronis, Art

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