Single-cell 3D genome reconstruction in the haploid setting using rigidity theory.

IF 2.3 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2025-03-29 DOI:10.1007/s00285-025-02203-2
Sean Dewar, Georg Grasegger, Kaie Kubjas, Fatemeh Mohammadi, Anthony Nixon
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Abstract

This article considers the problem of 3-dimensional genome reconstruction for single-cell data, and the uniqueness of such reconstructions in the setting of haploid organisms. We consider multiple graph models as representations of this problem, and use techniques from graph rigidity theory to determine identifiability. Biologically, our models come from Hi-C data, microscopy data, and combinations thereof. Mathematically, we use unit ball and sphere packing models, as well as models consisting of distance and inequality constraints. In each setting, we describe and/or derive new results on realisability and uniqueness. We then propose a 3D reconstruction method based on semidefinite programming and apply it to synthetic and real data sets using our models.

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利用刚性理论重建单倍体环境中的单细胞三维基因组
本文探讨了单细胞数据的三维基因组重构问题,以及在单倍体生物背景下这种重构的唯一性。我们将多个图模型视为该问题的表征,并使用图刚度理论的技术来确定可识别性。在生物学上,我们的模型来自 Hi-C 数据、显微镜数据及其组合。在数学上,我们使用单位球和球形堆积模型,以及由距离和不等式约束组成的模型。在每种情况下,我们都会描述和/或推导出关于可实现性和唯一性的新结果。然后,我们提出了一种基于半定量编程的三维重建方法,并利用我们的模型将其应用于合成和真实数据集。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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