THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data

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.

DeCOOC Deconvoluted Hi‐C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues

“…To assess deCOOC, we compared it with various state‐of‐the‐art deconvolution algorithms. In addition to Thunder, the sole Hi‐C deconvolution tool published, [ 12 ] we also compared eight transcriptome data‐oriented methods, that is, CS, [ 21 ] CDSeq, [ 17 ] DeconRNAseq, [ 22 ] DSA, [ 16 ] dtangle, [ 23 ] FARDEEP, [ 20 ] NNLS, [ 19 ] and ssKL. [ 26 ] Some tools can take bulk Hi‐C data as input directly, for example, CDseq, while others require additional cell type‐specific genes, for example, DSA and ssKL.…”

“…One way of computationally deriving cell type compositions from bulk Hi-C data involves deconvolution technologies. [9][10][11] To the best of our knowledge, except for Thunder, [12] we have yet to find other algorithms published for Hi-C data deconvolution. Thunder requires a list of predefined marker genes specifically expressed in cell types, that is, cell typespecific genes, while such gene lists are not always available.…”

Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants

“…Such multi-sample chromatin conformation data have emerged at the bulk level encompassing many cells (Gorkin et al, 2019;Chandra et al, 2021). Cell type deconvolution can be essential when analyzing multi-sample data from tissue samples to ensure valid inference and gain insights in a cell-type-specific manner (Figure 6) (Sefer et al, 2016;Rowland et al, 2022a). We anticipate future studies involving single-cell data, similar to multi-sample single-cell RNAsequencing data (Ren et al, 2021;Zheng et al, 2021), which can provide insights into disease etiology at an even more refined resolution (van Buren et al, 2021(van Buren et al, , 2022Zhang et al, 2022).…”

Understanding Regulatory Mechanisms of Brain Function and Disease through 3D Genome Organization

“…In addition, joint analysis of scHi-C data with bulk data can also allow us to take advantages of both types of data. For example, Rowland et al [95] identified expected cell-type-specific spatial organization profiles by deconvolving bulk Hi-C data from brain cortex. Naïve application of MUSIC [96] developed for RNA-seq data showed the advantage of integrative analysis with the matched scHi-C data.…”