Topologically associating domains (TAD) are a key structure of the 3D mammalian genomes. However, the prevalence and dynamics of TAD-like domains in single cells remain elusive. Here we develop a new algorithm, named deTOKI, to decode TAD-like domains with single-cell Hi-C data. By non-negative matrix factorization, deTOKI seeks regions that insulate the genome into blocks with minimal chance of clustering. deTOKI outperforms competing tools and reliably identifies TAD-like domains in single cells. Finally, we find that TAD-like domains are not only prevalent, but also subject to tight regulation in single cells.

Topologically associating domains (TAD) are functional chromatin units with hierarchical structure. However, the existence, prevalence and dynamics of such hierarchy in single cells remain unexplored. Here, we report a new generation TAD-like domain (TLD) detection algorithm, named deDoc2, to decode the hierarchy of TLDs in single cells. With dynamic programming, deDoc2 seeks genome partitions with global minimal structure entropy for both whole and local contact matrix. Compared to state-of-the-art tools, deDoc2 can uniquely identify the hierarchy of TLDs in single cells, in addition to outperforming its competitors. By applying deDoc2, we showed that the hierarchy of TLDs in single cells is highly dynamic during cell cycle, as well as among human brain cortex cells, and that it is associated with cellular identity and functions. Thus, our results demonstrated the abundance of information potentially encoded by TLD hierarchy for functional regulation. The deDoc2 can be freely accessed at https://github.com/zengguangjie/deDoc2.

Topologically associating domains (TADs) are functional chromatin units with hierarchical structure. However, the existence, prevalence, and dynamics of such hierarchy in single cells remain unexplored. Here, a new generation TAD‐like domain (TLD) detection algorithm, named deDoc2, to decode the hierarchy of TLDs in single cells, is reported. With dynamic programming, deDoc2 seeks genome partitions with global minimal structure entropy for both whole and local contact matrix. Notably, deDoc2 outperforms state‐of‐the‐art tools and is one of only two tools able to identify the hierarchy of TLDs in single cells. By applying deDoc2, it is showed that the hierarchy of TLDs in single cells is highly dynamic during cell cycle, as well as among human brain cortex cells, and that it is associated with cellular identity and functions. Thus, the results demonstrate the abundance of information potentially encoded by TLD hierarchy for functional regulation. The deDoc2 can be freely accessed at https://github.com/zengguangjie/deDoc2.

Topologically associating domains (TAD) are functional chromatin units with hierarchical structure. However, the existence, prevalence and dynamics of such hierarchy in single cells remain unexplored. Here, we report a new generation TAD-like domain (TLD) detection algorithm, named deDoc2, to decode the hierarchy of TLDs in single cells. With dynamic programming, deDoc2 seeks genome partitions with global minimal structure entropy for both whole and local contact matrix. Compared to state-of-the-art tools, deDoc2 can uniquely identify the hierarchy of TLDs in single cells, in addition to outperforming its competitors. By applying deDoc2, we showed that the hierarchy of TLDs in single cells is highly dynamic during cell cycle, as well as among human brain cortex cells, and that it is associated with cellular identity and functions. Thus, our results demonstrated the abundance of information potentially encoded by TLD hierarchy for functional regulation. The deDoc2 can be freely accessed at https://github.com/zengguangjie/deDoc2.