slideimp: efficient imputation of DNA methylation data.

Hung Pham; Adam P Lombroso; Esma Cansu Cevik; Hugh S Taylor; Kieran J O'Donnell

doi:10.1093/bioinformatics/btag318

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slideimp: efficient imputation of DNA methylation data.

Hung Pham·Adam P Lombroso·Esma Cansu Cevik·Hugh S Taylor·Kieran J O'Donnell

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Abstract

SUMMARY: We developed slideimp, an R package that extends and optimizes K-nearest neighbor (K-NN) and Principal Component Analysis (PCA) imputation with grouped and sliding-window modes for accurate and efficient imputation of microarray and whole-genome DNA methylation (DNAm) data, respectively. Under a realistic scenario, slideimp achieved ≈12-28× faster runtime and ≈3-6× peak memory usage reduction for DNAm microarray imputation (GSE286313, EPICv2, N = 72) and achieved high imputation accuracy in a whole-genome DNAm dataset (N = 41). AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/hhp94/slideimp_paper. The R package slideimp is available on CRAN (DOI: 10.32614/CRAN.package.slideimp). Version 1.0.0 of slideimp, which was used in this study, is archived on Zenodo (DOI: 10.5281/zenodo.20029382).

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DNA Methylation Software Oligonucleotide Array Sequence Analysis Principal Component Analysis Algorithms Humans Journal Article

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Hung Pham, Adam P Lombroso, Esma Cansu Cevik, Hugh S Taylor, Kieran J O'Donnell. 2026-06-01. slideimp: efficient imputation of DNA methylation data.. https://doi.org/10.1093/bioinformatics%2Fbtag318

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