ArticlePLoS genetics2022
THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data.
Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- Unico: a unified model for cell-type resolution genomics from heterogeneous omics data.Genome biology · 2025Article
- Gene expression and splicing QTL analysis of blood cells in African American participants from the Jackson Heart Study.Genetics · 2024Article
- SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model.Briefings in bioinformatics · 2024Article
- scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression.Bioinformatics (Oxford, England) · 2024Article
- The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data.Briefings in bioinformatics · 2023Review
- DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Dynamics of chromosome organization in a minimal bacterial cell.Frontiers in cell and developmental biology · 2023Article
- Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.Bioinformatics (Oxford, England) · 2022Article
- Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization.Genes · 2022Review
- Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants.Frontiers in cell and developmental biology · 2022Review
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
Abstract
Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to reference-free methods (e.g., TOAST, and NMF) and is more robust than reference-dependent methods (e.g. MuSiC). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.
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