Single-cell Hi-C (scHi-C) technology enables probing of higher-order chromatin structures in individual cells. It provides an opportunity to get a deeper insight into genomic compartmentalization changes of single cells across different conditions, paving the way to a common understanding of the interplay among compartmental organization, genome functions, and cellular phenotypes. Unfortunately, there are only a few methods currently available for the differential analysis of A/B compartments on Hi-C data at the bulk level; the computational analysis of compartmentalization changes at the single-cell level is a field in its infancy. Herein, we propose DeepExDC, an interpretable 1D convolutional neural network for differential analysis of A/B compartments in scHi-C data on a genome-wide scale. It accepts Hi-C contact matrices at the single-cell level, runs without any distribution assumption and differential pattern limitation, and interprets genomic compartmentalization changes across multiple conditions. The results on simulated and experimental scHi-C data show that our DeepExDC has higher accuracies in detecting different types of compartmentalization changes, and the interpretation values are demonstrated to be able to reflect compartment changes across cell types. It is also observed that the differential compartments given by DeepExDC agree well with those by state-of-the-art methods at the bulk level, help to characterize heterogeneity of single cells, and exhibit a reasonable biological relevance in multiple regards. In addition, considering that DeepExDC is free of distribution assumptions and differential patterns, we attempted to transfer it onto scRNA-seq and scATAC-seq data; it is interesting that our method also presents considerable power compared with the competing methods.

MOTIVATION: Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions. Unfortunately, it remains a challenge to identify TAD-like domains on single-cell Hi-C data due to its ultra-sparsity. RESULTS: We propose scKTLD, an in silico tool for the identification of TAD-like domains on single-cell Hi-C data. It takes Hi-C contact matrix as the adjacency matrix for a graph, embeds the graph structures into a low-dimensional space with the help of sparse matrix factorization followed by spectral propagation, and the TAD-like domains can be identified using a kernel-based changepoint detection in the embedding space. The results tell that our scKTLD is superior to the other methods on the sparse contact matrices, including downsampled bulk Hi-C data as well as simulated and experimental single-cell Hi-C data. Besides, we demonstrated the conservation of TAD-like domain boundaries at single-cell level apart from heterogeneity within and across cell types, and found that the boundaries with higher frequency across single cells are more enriched for architectural proteins and chromatin marks, and they preferentially occur at TAD boundaries in bulk cells, especially at those with higher hierarchical levels. AVAILABILITY AND IMPLEMENTATION: scKTLD is freely available at https://github.com/lhqxinghun/scKTLD.

MOTIVATION: The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic, since these matrices produced by the technology are inherently high dimensional and sparse, they suffer from noises and biases, and the topology of trajectory underlying them may be diverse. RESULTS: We present scHiCPTR, an unsupervised graph-based pipeline to infer pseudotime from single-cell Hi-C contact matrices. It provides a workflow consisting of imputation and embedding, graph construction, dual graph refinement, pseudotime calculation and result visualization. Beyond the few existing methods, scHiCPTR ties to optimize graph structure by two parallel procedures of graph pruning, which help reduce the spurious cell links resulted from noises and determine a global developmental directionality. Besides, it has an ability to handle developmental trajectories with multiple topologies, including linear, bifurcated and circular ones, and is competitive with methods developed for single-cell RNA-seq data. The comparative results tell that our scHiCPTR can achieve higher performance in pseudotime inference, and the inferred developmental trajectory exhibit a reasonable biological significance. AVAILABILITY AND IMPLEMENTATION: scHiCPTR is freely available at https://github.com/lhqxinghun/scHiCPTR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.