Paper Info
Title
Statistical methods for detecting differentially methylated regions based on MethylCap-seq data.
Abstract
DNA methylation is a well-established epigenetic mark, whose pattern throughout the genome, especially in the promoter or CpG islands, may be modified in a cell at a disease stage. Recently developed probabilistic approaches allow distributing methylation signals at nucleotide resolution from MethylCap-seq data. Standard statistical methods for detecting differential methylation suffer from 'curse of dimensionality' and sparsity in signals, resulting in high false-positive rates. Strong correlation of signals between CG sites also yields spurious results. In this article, we review applicability of high-dimensional mean vector tests for detection of differentially methylated regions (DMRs) and compare and contrast such tests with other methods for detecting DMRs. Comprehensive simulation studies are conducted to highlight the performance of these tests under different settings. Based on our observation, we make recommendations on the optimal test to use. We illustrate the superiority of mean vector tests in detecting cancer-related canonical gene pathways, which are significantly enriched for acute myeloid leukemia and ovarian cancer.
Year
DOI
Venue
2016
10.1093/bib/bbv089
BRIEFINGS IN BIOINFORMATICS
Keywords
Field
DocType
differentially methylated regions,methylCap-seq,high dimensionality,mean vector test
Differentially methylated regions,Genome,Data mining,Biology,CpG site,Epigenomics,Methylation,DNA methylation,Correlation,Bioinformatics,Epigenetics
Journal
Volume
Issue
ISSN
17
6
1467-5463
Citations 
PageRank 
References 
1
0.36
3
Authors
7
Name
Order
Citations
PageRank
d n ayyala110.36
David Frankhouser231.24
j o ganbat310.36
Guido Marcucci431.58
Ralf Bundschuh57713.85
Pearlly S Yan6262.51
shanming lin710.36