TY - JOUR
T1 - M 3 D
T2 - A kernel-based test for spatially correlated changes in methylation profiles
AU - Mayo, Tom R.
AU - Schweikert, Gabriele
AU - Sanguinetti, Guido
PY - 2015/3/15
Y1 - 2015/3/15
N2 - Motivation: DNA methylation is an intensely studied epigenetic mark implicated in many biological processes of direct clinical relevance. Although sequencing-based technologies are increasingly allowing high-resolution measurements of DNA methylation, statistical modelling of such data is still challenging. In particular, statistical identification of differentially methylated regions across different conditions poses unresolved challenges in accounting for spatial correlations within the statistical testing procedure. Results: We propose a non-parametric, kernel-based method, M 3 D, to detect higher order changes in methylation profiles, such as shape, across pre-defined regions. The test statistic explicitly accounts for differences in coverage levels between samples, thus handling in a principled way a major confounder in the analysis of methylation data. Empirical tests on real and simulated datasets show an increased power compared to established methods, as well as considerable robustness with respect to coverage and replication levels. Availability and implementation: R/Bioconductor package M 3 D.
AB - Motivation: DNA methylation is an intensely studied epigenetic mark implicated in many biological processes of direct clinical relevance. Although sequencing-based technologies are increasingly allowing high-resolution measurements of DNA methylation, statistical modelling of such data is still challenging. In particular, statistical identification of differentially methylated regions across different conditions poses unresolved challenges in accounting for spatial correlations within the statistical testing procedure. Results: We propose a non-parametric, kernel-based method, M 3 D, to detect higher order changes in methylation profiles, such as shape, across pre-defined regions. The test statistic explicitly accounts for differences in coverage levels between samples, thus handling in a principled way a major confounder in the analysis of methylation data. Empirical tests on real and simulated datasets show an increased power compared to established methods, as well as considerable robustness with respect to coverage and replication levels. Availability and implementation: R/Bioconductor package M 3 D.
UR - http://www.scopus.com/inward/record.url?scp=84925250091&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btu749
DO - 10.1093/bioinformatics/btu749
M3 - Article
C2 - 25398611
AN - SCOPUS:84925250091
VL - 31
SP - 809
EP - 816
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 6
ER -