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- W2794161738 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." @default.
- W2794161738 created "2018-03-29" @default.
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- W2794161738 date "2018-01-01" @default.
- W2794161738 modified "2023-09-26" @default.
- W2794161738 title "North Carolina Medical Student Views on Abortion" @default.
- W2794161738 doi "https://doi.org/10.18043/ncm.79.1.14" @default.
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