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

Xinjun Li1, Fan Feng2, Hongxi Pu3

  • 1Department of Statistics, University of Michigan, Ann Arbor, Michigan, United States of America.

Plos Computational Biology
|May 18, 2021
PubMed
Summary
This summary is machine-generated.

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This study introduces scHiCTools, an open-source software for analyzing single-cell Hi-C data. It addresses data sparsity and aids in understanding cell relationships through efficient embedding and visualization.

Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Single-cell Hi-C (scHi-C) enables studying 3D chromatin organization at the individual cell level.
  • Challenges include managing sparse contact maps and embedding single cells into lower dimensions.
  • Understanding cell relationships, like cell-cycle dynamics and differentiation, requires effective computational tools.

Purpose of the Study:

  • To present scHiCTools, an open-source computational toolbox for comprehensive and efficient analysis of single-cell Hi-C data.
  • To provide tools for addressing sparsity, cell similarity calculation, embedding, clustering, and visualization.
  • To facilitate deeper insights into single-cell chromatin organization and cellular heterogeneity.

Main Methods:

  • scHiCTools offers methods for single-cell screening and scHi-C data smoothing.

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  • Includes efficient algorithms for pairwise cell similarity calculation and cell embedding.
  • Provides multiple clustering methods and integrated visualization tools for 2D/3D embedding plots.

    • Main Results:

    • The toolbox integrates diverse computational methods for scHi-C data analysis.
    • Enables efficient processing and analysis of sparse single-cell Hi-C contact maps.
    • Facilitates the exploration of cellular relationships and heterogeneity through dimensionality reduction and clustering.

    Conclusions:

    • scHiCTools provides a robust and versatile platform for single-cell Hi-C data analysis.
    • The toolbox enhances the ability to investigate 3D genome structure and cellular dynamics at the single-cell level.
    • It is a cross-platform (Linux, macOS, Windows) open-source solution written in Python 3.