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.
Highlights
Statistical deconvolution methods have been applied extensively to studies of gene expression and DNA methylation to infer cell type proportions and estimate cell-type-specific profiles[1– 6]
In epigenome-wide association studies (EWAS) where the individual-level signal is a mixture of methylation profiles from different cell types, it has become standard practice to control for inferred cell type proportions when analyzing heterogeneous samples.[7]
We propose a non-negative matrix factorization (NMF) based Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER), to infer cell type proportions from bulk Hi-C data
Summary
THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data a1111111111 a1111111111 a1111111111 a1111111111 a1111111111. Data Availability Statement: Data from figures will be made available on the R Package’s github website: https://github.com/brycerowland/ thundeR. Bryce RowlandID1, Ruth HuhID1, Zoey Hou, Cheynna CrowleyID1, Jia WenID3, Yin ShenID4,5, Ming HuID6, Paola Giusti-RodrıguezID7, Patrick F.
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