TY - JOUR
T1 - DM-BLD
T2 - Differential methylation detection using a hierarchical Bayesian model exploiting local dependency
AU - Wang, Xiao
AU - Gu, Jinghua
AU - Hilakivi-Clarke, Leena
AU - Clarke, Robert
AU - Xuan, Jianhua
N1 - Publisher Copyright:
© The Author 2017. Published by Oxford University Press. All rights reserved.
PY - 2017/1/15
Y1 - 2017/1/15
N2 - Motivation: The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples. Results: We have developed a novel probabilistic approach, differential methylation detection using a hierarchical Bayesian model exploiting local dependency (DM-BLD), to detect differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation change in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables. Simulation studies demonstrate that DM-BLD outperforms existing methods for differential methylation detection, particularly when the methylation change is moderate and the variability of methylation in samples is high. DM-BLD has been applied to breast cancer data to identify important methylated genes (such as polycomb target genes and genes involved in transcription factor activity) associated with breast cancer recurrence.
AB - Motivation: The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples. Results: We have developed a novel probabilistic approach, differential methylation detection using a hierarchical Bayesian model exploiting local dependency (DM-BLD), to detect differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation change in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables. Simulation studies demonstrate that DM-BLD outperforms existing methods for differential methylation detection, particularly when the methylation change is moderate and the variability of methylation in samples is high. DM-BLD has been applied to breast cancer data to identify important methylated genes (such as polycomb target genes and genes involved in transcription factor activity) associated with breast cancer recurrence.
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U2 - 10.1093/bioinformatics/btw596
DO - 10.1093/bioinformatics/btw596
M3 - Article
C2 - 27616707
AN - SCOPUS:85028357492
SN - 1367-4803
VL - 33
SP - 161
EP - 168
JO - Bioinformatics
JF - Bioinformatics
IS - 2
ER -