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.
Highlights
Recent single-cell Hi-C sequencing technologies profile three-dimensional (3D) chromatin contact maps in individual cells, allowing us to characterize chromatin organization dynamics and cell-to-cell heterogeneity [1,2,3]
Single-cell Hi-C contact maps describe the numbers of interactions among genomic loci across the entire genome, and provide researchers 3D chromatin organization in each cell
To move beyond existing computational tools and methods to analyze and visualize single-cell Hi-C data, we present a software package, scHiCTools, which is implemented in Python
Summary
Single-cell Hi-C contact maps describe the numbers of interactions among genomic loci across the entire genome, and provide researchers 3D chromatin organization in each cell. To move beyond existing computational tools and methods to analyze and visualize single-cell Hi-C data, we present a software package, scHiCTools, which is implemented in Python. The software package provides researchers a collection of methods to investigate the cell-to-cell similarity based on their 3D chromatin organization, cluster cells into groups and visualize cells in two-dimensional or three-dimensional scatter plots. We apply scHiCTools to several single-cell Hi-C datasets to benchmark the performance of the methods provided in our toolbox, and present some plots generated using the software package. This is a PLOS Computational Biology Software paper
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