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Guidelines on optimizing DNA methylation reference panels for cell-type deconvolution
Xiaolong Guo1, Andrew E Teschendorff2
1Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China. guoxiaolong2022@sinh.ac.cn.
Communications Biology
|February 18, 2026
Summary
Optimizing DNA methylation reference panels is key for accurate cell-type deconvolution in epigenome-wide association studies. An effect-size optimization approach outperforms machine learning, especially when using hypomethylated markers for adult blood panels.
Area of Science:
- Epigenetics
- Bioinformatics
- Computational Biology
Background:
- Accurate cell-type deconvolution is essential for interpreting Epigenome-Wide Association Studies (EWAS).
- Estimating cell-type fractions typically relies on DNA methylation (DNAm) reference panels derived from sorted or single-cell data.
- Two primary methods exist for constructing these reference panels: machine learning and effect size/cell-type specificity optimization.
Purpose of the Study:
- To compare the performance of machine learning versus effect size optimization for building DNA methylation reference panels.
- To identify optimal strategies for constructing DNA methylation reference panels for improved cell-type deconvolution.
- To provide guidelines for future DNA methylation reference panel development.
Main Methods:
- Demonstrated the superiority of an effect size and cell-type specificity optimization approach over standard machine learning for reference panel construction.
- Evaluated panel performance on independent datasets, highlighting overfitting issues with machine learning due to small sorted sample sizes.
- Compared DNA methylation reference panels built from hypomethylated versus hypermethylated markers for adult blood cell types.
Main Results:
- The effect size optimization method demonstrated superior performance and reduced overfitting compared to machine learning approaches.
- DNA methylation reference panels built using cell-type specific hypomethylated markers resulted in improved estimation of cell-type fractions in adult blood.
- Standard machine learning models tended to overfit and underperform on independent data when trained on limited sorted samples.
Conclusions:
- The effect size optimization approach is preferable for building DNA methylation reference panels due to its robustness against overfitting.
- Utilizing hypomethylated markers in adult blood panels enhances the accuracy of cell-type fraction estimation.
- These findings offer critical guidelines for developing more accurate DNA methylation reference panels and include an optimized panel-building function in the EpiDISH R-package.
