North Carolina Medical Student Views on Abortion
Year: 2018
Type: article
Abstract: Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. To effectively leverage Hi-C data for biological insights, we need to control for the 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 real data based simulations to test *THUNDER* constructed from published single-cell Hi-C (scHi-C) data. *THUNDER* more accurately estimates the underlying cell type proportions when compared to both supervised and unsupervised deconvolution methods including CIBERSORT, TOAST, and NMF. *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* estimates cell-type-specific chromatin contact profiles for all cell types in bulk Hi-C mixtures. These estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still sparse.
Source: North Carolina Medical Journal
DOI: 10.18043/ncm.79.1.14
Authors: Christopher L. Bennett, David A. McDonald, Alex Finch, Stuart Rennie, Jessica E. Morse
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W2794161738