Abstract

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

Highlights

  • DNA methylation, one of the most important epigenetic factors, has been intensively investigated, and its influence in a variety of human diseases, most notably cancer, has been firmly established [1,2]

  • Identification of Consistently Differentially Methylated Regions (ICDMR): Clustering methylation data by a normal mixture model In order to better estimate methylation status in studied samples, we propose to exploit the bimodal distribution of M

  • The simulation study was first carried out under the null hypothesis, i.e., there is no differentially methylated region (DMR) among the samples, using data generated from the AR(1) model and with r values set at 0, 0.3, 0.5, and 0.7

Read more

Summary

Introduction

DNA methylation, one of the most important epigenetic factors, has been intensively investigated, and its influence in a variety of human diseases, most notably cancer, has been firmly established [1,2]. Many studies have been undertaken of the former type, in which differences in methylation levels have been explored in individuals with different phenotypic labels, such as diseased and healthy tissues. In such cases, the traditional Student’s t-test and Wilcoxon Rank Sum Test (WRST) [7,8] can be used to find DMRs, using normalized methylation levels between two groups; this has been done using the conventional univariate test for differential expression analysis. An analysis of variance (ANOVA) model, relying on raw intensity data, has been developed to identify aberrant methylation patterns for oligodendroglioma and breast cancer samples, respectively [9], and ‘‘sliding window’’ approaches, in which various window sizes are used, have been proposed for methylation segment analyses [10]

Methods
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.