Alexander Dietrich, Lina-Liv Willruth, Korbinian Pürckhauer, Carlos Oltmanns, Moana Witte, Sebastian Klein, Anke R M Kraft, Markus Cornberg, Markus List
{"title":"Unifying DNA methylation-based <i>in silico</i> cell-type deconvolution with <i>deconvMe</i>.","authors":"Alexander Dietrich, Lina-Liv Willruth, Korbinian Pürckhauer, Carlos Oltmanns, Moana Witte, Sebastian Klein, Anke R M Kraft, Markus Cornberg, Markus List","doi":"10.1093/bioadv/vbaf201","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>Cell-type deconvolution is widely applied to gene expression and DNA methylation data, but access to methods for the latter remains limited. We introduce <i>deconvMe</i>, a new R package that simplifies access to DNA methylation-based deconvolution methods predominantly for blood data, and we additionally compare their estimates to those from gene expression and experimental ground truth data using a unique matched blood dataset.</p><p><strong>Availability and implementation: </strong><i>DevonMe</i> is available at https://github.com/omnideconv/deconvMe, the processed blood data is available at https://figshare.com/articles/dataset/methyldeconv_data/28563854/3.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"5 1","pages":"vbaf201"},"PeriodicalIF":2.8000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12417072/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbaf201","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Summary: Cell-type deconvolution is widely applied to gene expression and DNA methylation data, but access to methods for the latter remains limited. We introduce deconvMe, a new R package that simplifies access to DNA methylation-based deconvolution methods predominantly for blood data, and we additionally compare their estimates to those from gene expression and experimental ground truth data using a unique matched blood dataset.
Availability and implementation: DevonMe is available at https://github.com/omnideconv/deconvMe, the processed blood data is available at https://figshare.com/articles/dataset/methyldeconv_data/28563854/3.