Published December 1, 2025 | Version 0.2.0
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dmeth: A comprehensive Python toolkit for differential DNA methylation analysis with empirical Bayes moderation and biomarker discovery

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Description

A fast, statistically rigorous Python framework providing a toolkit for DNA methylation analysis - from raw beta matrices to biomarkers and functional interpretation. dmeth implements the full modern differential methylation pipeline used in high-impact epigenome-wide association studies (EWAS), with performance and correctness on par with established R/bioconductor tools, all in pure Python.

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Additional details

Software

Repository URL
https://github.com/dare-afolabi/dmeth/
Programming language
Python

References

  • Smyth, G. K. (2004). Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3(1).
  • Liu, P., & Hwang, J.T.G. (2007). Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics, 23(6), 739–746
  • Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W.A., Hou, L., & Lin, S. (2010). Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. *BMC Bioinformatics*, 11:587.
  • Jung, S.H., Young, S.S. (2012). Power and sample size calculation for microarray studies. Journal of Biopharmaceutical Statistics, 22(1):30-42.
  • Phipson, B. et al. (2016). missMethyl: an R package for analyzing data from Illumina's HumanMethylation450 platform. Bioinformatics, 32(2), 286-288.