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Deciphering single-cell 3D chromatin structure using scCTG
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AI
DOI：10.1063/5.0241334.png)
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摘要
En 中文
Sequencing-based Hi-C technology has been widely used to study the three-dimensional structure of chromatin. More recently, the development of single-cell Hi-C technology has enabled the study of chromatin structural variations between individual cells. However, single-cell Hi-C data are often highly sparse, necessitating the use of imputation algorithms to address insufficient sampling. Current methods encounter challenges such as significant discrepancies from real structural features, limited reproducibility, slower computational speeds, or reliance on large amounts of training data, which hinder their broader applicability. In this study, we improved the previously published C(T)G (Hi-C To Geometry) algorithm to introduce the single-cell C(T)G (scC(T)G) algorithm, which combines convolution and diffusion processes to yield the spatial distance matrix for various types of single-cell chromatin structure data. scC(T)G algorithm shows a good performance in terms of computational efficiency, robustness, and correlation with physical spatial distances. The scC(T)G algorithm can be applied to effectively identify compartments and insulation strength for each locus, providing deeper insights into the relationship between chromatin structure and gene expression at the single-cell level.
Keyword:
HI-C
GENOME STRUCTURES
REVEALS