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Computational deconvolution of DNA methylation data from mixed DNA samples.

Maísa R Ferro Dos Santos1,2, Edoardo Giuili1,2, Andries De Koker1,2

  • 1VIB-UGent Center for Medical Biotechnology (CMB), Technologiepark-Zwijnaarde 75, 9052 Zwijnaarde, Belgium.

Briefings in Bioinformatics
|May 19, 2024
PubMed
Summary

This review compares 25 computational tools for DNA methylation (DNAm) deconvolution, estimating cell proportions in mixed samples. It guides method selection based on data type and availability for accurate biological insights.

Keywords:
DNA methylation profilingcomputational deconvolutiontool comparison

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Area of Science:

  • Computational Biology
  • Epigenetics
  • Bioinformatics

Background:

  • Bulk DNA methylation (DNAm) data analysis requires deconvolution to estimate cell-type proportions.
  • Numerous computational tools have been developed for DNAm deconvolution, necessitating a comparative overview.

Purpose of the Study:

  • To comprehensively review and compare 25 DNA methylation deconvolution methods published between 2012 and 2023.
  • To assess the impact of data generation platforms, pre-processing, and reference datasets on deconvolution performance.
  • To provide guidelines for selecting appropriate deconvolution methods based on data characteristics.

Main Methods:

  • Systematic review and comparison of 25 DNA methylation deconvolution algorithms.
  • Categorization of methods into supervised, unsupervised, and hybrid approaches.
  • Analysis of reference-based, partial-reference, and reference-free deconvolution strategies.

Main Results:

  • Detailed comparison of strengths and limitations for each of the 25 reviewed deconvolution methods.
  • Evaluation of how microarray vs. sequencing platforms influence deconvolution outcomes.
  • Assessment of pre-processing steps and reference dataset choice on method performance.

Conclusions:

  • The choice of DNA methylation deconvolution tool significantly impacts cell-type proportion estimation.
  • Guidelines are provided to aid researchers in selecting optimal methods based on data type, availability, and experimental context.
  • This review serves as a crucial resource for advancing DNA methylation data analysis.