Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, Illinois, United States of America.
PLoS Genet. 2022 Mar 8;18(3):e1010102. doi: 10.1371/journal.pgen.1010102. eCollection 2022 Mar.
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.
Hi-C 数据提供了在批量样本中跨细胞类型和状态的三维染色质接触的群体平均估计。有效分析 Hi-C 数据需要控制异质批量样本中不同细胞类型比例的潜在混杂因素。我们提出了一种从批量 Hi-C 数据中推断细胞类型组成的新的无监督去卷积方法，即两步 Hi-c UNsupervised DEconvolution appRoach（THUNDER）。我们进行了广泛的模拟，以基于结合两个已发表的单细胞 Hi-C（scHi-C）数据集来测试 THUNDER。THUNDER 与无参考方法（例如 TOAST 和 NMF）相比，更准确地估计了潜在的细胞类型比例，并且比基于参考的方法（例如 MuSiC）更稳健。我们进一步证明了 THUNDER 在估计细胞类型比例和识别成人皮质组织样本中的 Hi-C 数据中的细胞类型特异性相互作用方面的实际效用。THUNDER 将是调整群体样本中不同细胞类型组成的有用工具，有助于进行有效的和更强大的下游分析，例如差异染色质组织研究。此外，THUNDER 估计的接触谱提供了一个有用的探索性框架，用于研究染色质互作组的细胞类型特异性，而实验数据仍然很少。
