THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data Public Deposited

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  • Rowland, Bryce
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Huh, Ruth
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Hou, Zoey
    • Other Affiliation: Northwestern University
  • Crowley, Cheynna
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Wen, Jia
    • Affiliation: School of Medicine, Department of Genetics
  • Shen, Yin
    • Other Affiliation: University of California San Francisco
  • Hu, Ming
    • Other Affiliation: Cleveland Clinic Foundation
  • Giusti-Rodríguez, Paola
    • Other Affiliation: University of Florida College of Medicine
  • Sullivan, Patrick F.
    • Affiliation: School of Medicine, Department of Genetics
  • Li, Yun
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
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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  • Article
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  • In Copyright
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  • Attribution 4.0 International
Journal title
  • PLOS Genetics
Journal volume
  • 18
Journal issue
  • 3
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  • e1010102
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  • English
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  • 1553-7404
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