# A systematic evaluation of cell-type-specific differential methylation analysis in bulk tissue

## TL;DR

This paper evaluates different computational models for identifying cell-type-specific DNA methylation changes in bulk tissue data and finds that combining model results improves performance.

## Contribution

The novel contribution is proposing and demonstrating the effectiveness of aggregating model results using minimum and average p-value methods for cell-type-specific differential methylation analysis.

## Key findings

- Computational models for cell-type-specific differential methylation vary in performance across metrics and sample sizes.
- Aggregation methods like minpv and avepv significantly improve the identification of cell-type-specific differential methylation CpGs.
- Simulation and case study evaluations reveal strengths and limitations of each model.

## Abstract

We conducted a systematic assessment of computational models—CellDMC, TCA, HIRE, TOAST, and CeDAR—for detecting cell-type-specific differential methylation CpGs in bulk methylation data profiled using the Illumina DNA Methylation BeadArrays. This assessment was performed through simulations and case studies involving two epigenome-wide association studies (EWAS) on rheumatoid arthritis and major depressive disorder. Our evaluation provided insights into the strengths and limitations of each model. The results revealed that the models varied in performance across different metrics, sample sizes, and computational efficiency. Additionally, we proposed integrating the results from these models using the minimum p-value (\documentclass[12pt]{minimal}
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$minpv$\end{document}) and average p-value (\documentclass[12pt]{minimal}
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$avepv$\end{document}) approaches. Our findings demonstrated that these aggregation methods significantly improved performance in identifying cell-type-specific differential methylation CpGs.