An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. (17th September 2018)

• Record Type: Journal Article
• Authors: Mallik, Saurav; Odom, Gabriel J; Gao, Zhen; Gomez, Lissette; Chen, Xi; Wang, Lily
• Is Part Of: Briefings in bioinformatics. Volume 20:Number 6(2020)
• Page Start: 2224
• Page End: 2235
• Publication Date: 2018-09-17
• Subjects: DMR identification -- DNA methylation -- epigenome-wide association studies -- software comparison; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby085
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.