scHiCTools: A computational toolbox for analyzing single-cell Hi-C data

Abstract

Single-cell Hi-C (scHi-C) sequencing technologies allow us to investigate three-dimensional chromatin organization at the single-cell level. However, we still need computational tools to deal with the sparsity of the contact maps from single cells and embed single cells in a lower-dimensional Euclidean space. This embedding helps us understand relationships between the cells in different dimensions, such as cell-cycle dynamics and cell differentiation. We present an open-source computational toolbox, scHiCTools, for analyzing single-cell Hi-C data comprehensively and efficiently. The toolbox provides two methods for screening single cells, three common methods for smoothing scHi-C data, three efficient methods for calculating the pairwise similarity of cells, three methods for embedding single cells, three methods for clustering cells, and a build-in function to visualize the cells embedding in a two-dimensional or three-dimensional plot. scHiCTools, written in Python3, is compatible with different platforms, including Linux, macOS, and Windows.

Single-cell Hi-C data analysis: safety in numbers

“…types of pronuclei in the mouse zygote [ 28 ] or types of cells forming the dataset [ 76 ]). Each cluster, or group of cells, is assigned with a particular cell type and the quality is usually assessed by normalised mutual information [ 62 ] or adjusted rand score [ 62 , 100 ].…”

Mapping nucleosome and chromatin architectures: A survey of computational methods

“…Quality control of sequencing data is crucial to avoid technical artifacts. Despite of these challenges, new sets of computational methods have been developed for processing scHi-C data to reconstruct single-cell 3D chromatin structures [107] , [108] , [109] , to impute the chromosome contact matrices [110] , [111] , [112] , to identify TAD-like domains [113] , to classify single cells [114] , to identify chromatin loops [115] , and to provide toolbox of scHi-C [116] .…”

Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle

“…Notably, the only method that can integrate scHi-C with scRNA-seq is scGAD (Shen et al, 2022). However, scGAD's common feature-based integration approach does not capitalize on the simultaneous profiling of 3D conformation and DNA methylation status of cells as enabled by sn-m3C-seq 2 (Lee et al, 2019;Liu et al, 2021) and scMethyl-HiC (Li et al, 2019a). In contrast, Higashi (Zhang et al, 2022a) facilitates joint analysis of scHi-C and DNA methylation data; however, the inference implemented is limited to cell type clustering and lacks downstream analysis, and its practical utility is hindered by its computational requirements, which led to development of Fast-Higashi (Zhang et al, 2022b).…”

“…Simulation studies comparing the joint analysis of Muscle to a baseline strategy of flatting out the tensor as a matrix and leveraging matrix decomposition reveal consistently better performance by Muscle and supports the robustness of Muscle to a wide range of signal-to-noise levels. Muscle in the joint analysis mode for the sn-m3C-seq data (Lee et al, 2019;Liu et al, 2021) or scMethyl-HiC data (Li et al, 2019a) successfully identifies cell type specific associations between DNA methylation profiles and 3D genome structure including TAD boundaries and compartment territories. Collectively, Muscle represents a significant modeling advancement in the joint analysis of scHi-C data with other data modalities.…”

“…These new approaches have the potential to elucidate the interplay between the epigenetic mechanisms and 3D genome structure in a wide variety of biological contexts. Statistical and computational approaches for specific scHi-C data inference tasks are appearing rapidly (e.g., scHiCluster (Zhou et al, 2019), scHiC Topics (Kim et al, 2020), Higashi (Zhang et al, 2022a), BandNorm and 3DVI (Zheng et al, 2022), and Fast-Higashi (Zhang et al, 2022b), SnapHiC (Yu et al, 2021), scHiCTools (Li et al, 2021), DeTOKI (Li et al, 2021)). However, computational tools for integrating scHi-C with other data modalities such as transcriptomics, epigenomics, and epigenetics are lagging behind.…”