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Systematic evaluation of methylation-based cell type deconvolution methods for plasma cell-free DNA.

Tongyue Sun1, Jinqi Yuan1, Yacheng Zhu1

  • 1School of Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou, 215123, China.

Genome Biology
|December 20, 2024
PubMed
Summary

This study benchmarks methylation-based methods for cell type deconvolution of cell-free DNA (cfDNA). Performance depends on reference data completeness and sequencing depth, guiding optimal method selection for cfDNA analysis.

Keywords:
BenchmarkCell-free DNADNA methylationDeconvolution

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Area of Science:

  • Biomarker Discovery
  • Genomics
  • Computational Biology

Background:

  • Cell-free DNA (cfDNA) originates from tissue breakdown and offers noninvasive insights into disease.
  • cfDNA analysis can reveal tissue homeostasis changes, aiding disease detection and treatment monitoring.
  • Methylation-based cfDNA cell type deconvolution is promising but lacks systematic evaluation.

Purpose of the Study:

  • To comprehensively benchmark existing methylation-based cfDNA cell type deconvolution methods.
  • To identify key factors influencing the accuracy of cfDNA deconvolution.
  • To provide guidelines for selecting appropriate methods for cfDNA analysis.

Main Methods:

  • Benchmarking of five deconvolution tools: MethAtlas, cfNOMe toolkit, CelFiE, CelFEER, and UXM.
  • Generation of in silico cfDNA samples using deep whole-genome bisulfite sequencing data from 35 human cell types.
  • Evaluation of method performance under varying conditions, including reference completeness and sequencing depth, using real-world datasets.

Main Results:

  • Deconvolution performance is significantly influenced by reference marker selection, sequencing depth, and atlas completeness.
  • Incomplete reference datasets with missing markers or cell types lead to suboptimal deconvolution results.
  • Performance varied across methods and conditions, highlighting the need for tailored analysis strategies.

Conclusions:

  • Developed guidelines for selecting cfDNA deconvolution methods based on data characteristics.
  • Recommendations consider cfDNA sequencing depth and reference atlas completeness to optimize deconvolution performance.
  • Aimed to enhance the clinical utility of methylation-based cfDNA analysis through informed method selection.