HOMED enables hierarchical and multimodal optimization of DNA methylation deconvolution across tissues
- 1University of Alabama at Birmingham
Abstract
Cellular heterogeneity is a major confounder in bulk DNA methylation data used in epigenome-wide association studies. Existing reference-based DNAm deconvolution methods often overlook hierarchical relationships among related cell types and may generalize poorly across datasets due to limited variability in reference profiles. We developed HOMED (Hierarchically Optimized Methylation Deconvolution), a framework that integrates cell-type hierarchy, single-cell RNA sequencing-guided deconvolution, and paired bulk RNA-seq/DNAm data for CpG signature optimization. Across multiple simulated and real heterogeneous tissue datasets, HOMED achieved a 68% higher mean Pearson correlation coefficient (PCC) and 60% lower mean root mean squared error (RMSE), compared to other scRNA-seq-guided or single-cell DNA methylation-based DNAm deconvolution methods. Moreover, application of HOMED to a smoking-exposed placenta DNA methylation cohort revealed significant shifts in cell-type compositions in endothelial, nRBC, stromal, and villous trophoblast proportions. Adjusting for these compositional differences reduced inflated smoking-associated CpGs by nearly three folds, and doubled the number of enriched pathways with known associations to smoking. In summary, HOMED is an accurate and practical method to infer cell-type proportions from bulk DNA methylation data.
Related
- Hierarchical Uncertainty Transformation During Multimodal Comprehension
- Hierarchical distribution matching enables comprehensive characterization of common and condition-specific cell niches in spatial omics data
- stEDGE enables edge-guided multiscale reconstruction of hierarchical spatial domains and transition interfaces in spatial transcriptomics
- Multimodal cellular context enables molecular phenotyping from single-molecule trajectories
- The perioperative sensory–cognitive interface: multimodal neuroimaging of hierarchical network reconfiguration in perioperative neurocognitive disorders
BibTeX
@misc{liu2026homed,
title = {{HOMED enables hierarchical and multimodal optimization of DNA methylation deconvolution across tissues}},
author = {Y. Liu and Y. Chen and Y. Du and L. Garmire},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.08.730896},
url = {https://www.biorxiv.org/content/10.64898/2026.06.08.730896v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - HOMED enables hierarchical and multimodal optimization of DNA methylation deconvolution across tissues
AU - Y. Liu
AU - Y. Chen
AU - Y. Du
AU - L. Garmire
PY - 2026
DA - 2026/06/08
PB - bioRxiv
DO - 10.64898/2026.06.08.730896
UR - https://www.biorxiv.org/content/10.64898/2026.06.08.730896v2
AB - Cellular heterogeneity is a major confounder in bulk DNA methylation data used in epigenome-wide association studies. Existing reference-based DNAm deconvolution methods often overlook hierarchical relationships among related cell types and may generalize poorly across datasets due to limited variability in reference profiles. We developed HOMED (Hierarchically Optimized Methylation Deconvolution), a framework that integrates cell-type hierarchy, single-cell RNA sequencing-guided deconvolution, and paired bulk RNA-seq/DNAm data for CpG signature optimization. Across multiple simulated and real heterogeneous tissue datasets, HOMED achieved a 68% higher mean Pearson correlation coefficient (PCC) and 60% lower mean root mean squared error (RMSE), compared to other scRNA-seq-guided or single-cell DNA methylation-based DNAm deconvolution methods. Moreover, application of HOMED to a smoking-exposed placenta DNA methylation cohort revealed significant shifts in cell-type compositions in endothelial, nRBC, stromal, and villous trophoblast proportions. Adjusting for these compositional differences reduced inflated smoking-associated CpGs by nearly three folds, and doubled the number of enriched pathways with known associations to smoking. In summary, HOMED is an accurate and practical method to infer cell-type proportions from bulk DNA methylation data.
SN - 2692-8205
ER -