Cargando…
THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data
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 p...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932604/ https://www.ncbi.nlm.nih.gov/pubmed/35259165 http://dx.doi.org/10.1371/journal.pgen.1010102 |
_version_ | 1784671477171224576 |
---|---|
author | Rowland, Bryce Huh, Ruth Hou, Zoey Crowley, Cheynna Wen, Jia Shen, Yin Hu, Ming Giusti-Rodríguez, Paola Sullivan, Patrick F. Li, Yun |
author_facet | Rowland, Bryce Huh, Ruth Hou, Zoey Crowley, Cheynna Wen, Jia Shen, Yin Hu, Ming Giusti-Rodríguez, Paola Sullivan, Patrick F. Li, Yun |
author_sort | Rowland, Bryce |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8932604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89326042022-03-19 THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data Rowland, Bryce Huh, Ruth Hou, Zoey Crowley, Cheynna Wen, Jia Shen, Yin Hu, Ming Giusti-Rodríguez, Paola Sullivan, Patrick F. Li, Yun PLoS Genet Methods 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. Public Library of Science 2022-03-08 /pmc/articles/PMC8932604/ /pubmed/35259165 http://dx.doi.org/10.1371/journal.pgen.1010102 Text en © 2022 Rowland et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Methods Rowland, Bryce Huh, Ruth Hou, Zoey Crowley, Cheynna Wen, Jia Shen, Yin Hu, Ming Giusti-Rodríguez, Paola Sullivan, Patrick F. Li, Yun THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data |
title | THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data |
title_full | THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data |
title_fullStr | THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data |
title_full_unstemmed | THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data |
title_short | THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data |
title_sort | thunder: a reference-free deconvolution method to infer cell type proportions from bulk hi-c data |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8932604/ https://www.ncbi.nlm.nih.gov/pubmed/35259165 http://dx.doi.org/10.1371/journal.pgen.1010102 |
work_keys_str_mv | AT rowlandbryce thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT huhruth thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT houzoey thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT crowleycheynna thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT wenjia thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT shenyin thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT huming thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT giustirodriguezpaola thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT sullivanpatrickf thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata AT liyun thunderareferencefreedeconvolutionmethodtoinfercelltypeproportionsfrombulkhicdata |