Unifying DNA methylation-based in silico cell-type deconvolution with deconvMe

Alexander Dietrich; Lina Liv Willruth; Korbinian Purckhauer; Carlos Oltmanns; Moana Witte; Sebastian Klein; Anke R.M. Kraft; Markus Cornberg; Markus List

doi:10.1093/bioadv/vbaf201

Unifying DNA methylation-based in silico cell-type deconvolution with deconvMe

Alexander Dietrich
, Lina Liv Willruth
, Korbinian Purckhauer
, Carlos Oltmanns
, Moana Witte
, Sebastian Klein
, Anke R.M. Kraft
, Markus Cornberg
, Markus List

Assistant Professorship of Data Science in Systems Biology

Technical University of Munich
Medizinische Hochschule Hannover
Gottfried Wilhelm Leibniz Universität Hannover
Helmholtz Centre for Infection Research (HZI)

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	vbaf201
Journal	Bioinformatics Advances
Volume	5
Issue number	1
DOIs	https://doi.org/10.1093/bioadv/vbaf201
State	Published - 2025

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10.1093/bioadv/vbaf201

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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.",

author = "Alexander Dietrich and Willruth, \{Lina Liv\} and Korbinian Purckhauer and Carlos Oltmanns and Moana Witte and Sebastian Klein and Kraft, \{Anke R.M.\} and Markus Cornberg and Markus List",

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AU - Dietrich, Alexander

AU - Willruth, Lina Liv

AU - Purckhauer, Korbinian

AU - Oltmanns, Carlos

AU - Witte, Moana

AU - Klein, Sebastian

AU - Kraft, Anke R.M.

AU - Cornberg, Markus

AU - List, Markus

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/105015501199

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DO - 10.1093/bioadv/vbaf201

M3 - Article

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JO - Bioinformatics Advances

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Unifying DNA methylation-based in silico cell-type deconvolution with deconvMe

Abstract

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