Computational Deconvolution of DNA Methylation Data from Mixed DNA Samples

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2024 May 19

PMID 38762790

Authors

Maisa R Ferro Dos Santos

Edoardo Giuili

Andries De Koker

Celine Everaert

Katleen De Preter

Affiliations

Soon will be listed here.

Abstract

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

Citing Articles

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