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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.<\/jats:p>","DOI":"10.1093\/bib\/bbae234","type":"journal-article","created":{"date-parts":[[2024,5,19]],"date-time":"2024-05-19T11:07:53Z","timestamp":1716116873000},"source":"Crossref","is-referenced-by-count":14,"title":["Computational deconvolution of DNA methylation data from mixed DNA samples"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1336-5434","authenticated-orcid":false,"given":"Ma\u00edsa R","family":"Ferro dos Santos","sequence":"first","affiliation":[{"name":"VIB-UGent Center for Medical Biotechnology (CMB) , Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde , Belgium"},{"name":"Cancer Research Institute Ghent (CRIG) , 9000 Ghent , 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