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Related Experiment Video

Updated: Aug 31, 2025

Methyl-binding DNA capture Sequencing for Patient Tissues
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Deconvolution of tumor composition using partially available DNA methylation data.

Dingqin He1,2, Ming Chen1,2, Wenjuan Wang1,2

  • 1College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.

BMC Bioinformatics
|August 24, 2022
PubMed
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PRMeth accurately deconvolves tumor cell proportions using partial DNA methylation data, overcoming limitations of traditional methods for cancer immunotherapy research.

Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Tumor heterogeneity analysis and immunotherapy response prediction require understanding cell type proportions.
  • Experimental methods for cell proportion measurement are costly and prone to data loss.
  • Large-scale DNA methylation data enables computational prediction of cell proportions.

Purpose of the Study:

  • To develop a computational method for deconvolving tumor cell mixtures using partially available DNA methylation data.
  • To simultaneously estimate cell proportions and methylation profiles of unknown cell types.

Main Methods:

  • Proposed PRMeth, a method utilizing an iteratively optimized non-negative matrix factorization framework.
  • Input: DNA methylation profiles of a subset of cell types in tissue mixtures.
Keywords:
Cell population proportionsDNA methylation dataImmunotherapyNon-negative matrix factorizationTumor heterogeneity

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  • Output: Estimated proportions of all cell types and methylation profiles of unknown cell types.

    • Main Results:

    • PRMeth effectively infers cell type proportions and recovers methylation profiles of unknown cell types.
    • Comparison with five existing methods across three benchmark datasets demonstrated PRMeth's superior performance.
    • Application to TCGA tumor data showed immune cell proportions consistent with prior studies and biologically significant.

    Conclusions:

    • PRMeth overcomes the challenge of incomplete DNA methylation reference data, achieving high deconvolution accuracy.
    • The method facilitates novel research directions in cancer immunotherapy.
    • PRMeth is implemented in R and available on GitHub for public use.