scHiCTools: A computational toolbox for analyzing single-cell Hi-C data |
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Authors: | Xinjun Li Fan Feng Hongxi Pu Wai Yan Leung Jie Liu |
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Affiliation: | 1. Department of Statistics, University of Michigan, Ann Arbor, Michigan, United States of America ; 2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America ; 3. College of Literature Science, and the Arts, University of Michigan, Ann Arbor, Michigan, United States of America ; Johns Hopkins University, UNITED STATES |
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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. |
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