SpectralTAD: an R package for defining a hierarchy of Topologically Associated Domains using spectral clustering

Abstract

The three-dimensional (3D) structure of the genome plays a crucial role in regulating gene expression. Chromatin conformation capture technologies (Hi-C) have revealed that the genome is organized in a hierarchy of topologically associated domains (TADs), the fundamental building blocks of the genome. Identifying such hierarchical structures is a critical step in understanding regulatory interactions within the genome. Existing tools for TAD calling frequently require tunable parameters, are sensitive to biases such as sequencing depth, resolution, and sparsity of Hi-C data, and are computationally inefficient. Furthermore, the choice of TAD callers within the R/Bioconductor ecosystem is limited. To address these challenges, we frame the problem of TAD detection in a spectral clustering framework. Our SpectralTAD R package has automatic parameter selection, robust to sequencing depth, resolution and sparsity of Hi-C data, and detects hierarchical, biologically relevant TAD structure. Using simulated and real-life Hi-C data, we show that SpectralTAD outperforms rGMAP and TopDom, two state-of-the-art R-based TAD callers. TAD boundaries that are shared among multiple levels of the hierarchy were more enriched in relevant genomic annotations, e.g., CTCF binding sites, suggesting their higher biological importance. In contrast, boundaries of primary TADs, defined as TADs which cannot be split into sub-TADs, were found to be less enriched in genomic annotations, suggesting their more dynamic role in genome regulation. In summary, we present a simple, fast, and user-friendly R package for robust detection of TAD hierarchies supported by biological evidence. SpectralTAD is available on https://github.com/dozmorovlab/SpectralTAD and Bioconductor (submitted).