Abstract
A number of empirical Bayes models were developed to investigate the differential methylation analysis. However, it is not clear which empirical Bayes model performs best in differential methylation analysis. In this paper, five empirical Bayes models were constructed and applied to the differential methylation analysis of A2780 cells between before and after 1, 3, and 5 round of cisplatin treatment. The log-normal model with the background variance showed the lowest minimized negative log-likelihood. It inferred increasing number of differentially methylated loci from 1 to 3 to 5 rounds of cisplatin treatment on the A2780 cells, which was consistent to cisplatin resistant IC50 data. Among differentially methylated loci selected from each empirical model, three time dependent methylation patterns were defined: stochastic hypomethylation, stochastic hypermethylation, and random methylation. If the empirical Bayes model of the DNA methylation assumed log-normal distribution, both stochastically hypomethylated loci and stochastically hypermethylated loci were enriched with a number of transcription factor binding sites. Almost no TFBS enrichment was observed if the gamma distribution was assumed in the empirical Bayes model. In summary, by comparing the performances of the differential methylation analysis and the TFBS enrichment analysis, log-normal distribution is a better statistical assumption than the gamma distribution in the empirical Bayes model.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.