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在Hi-C接触矩阵上随机步行可以提高数据质量吗? 一个评估评估

Yongqi Liu¹, Shili Lin¹

¹Department of Statistics, The Ohio State University, Columbus, OH 43210.

bioRxiv : the preprint server for biology

|July 16, 2025

概括

随机步行改善Hi-C数据质量的方法,如随机步行与步骤 (RWS) 和随机步行与重新启动 (RWR),对于识别像TADs这样的染色体结构几乎没有好处. 即使具有最佳参数,这些方法也不能显著提高下游分析.

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
分子生物学分子生物学

背景情况:

高通量染色体构造捕获 (Hi-C) 和单细胞 Hi-C (scHi-C) 对于研究全基因组染色体组织,包括区,TAD和长距离相互作用至关重要.
数据质量,特别是scHi-C的稀疏性,需要在生物分析之前改进方法.
基于随机步行的方法,包括RWS和RWR,已被开发用于提高数据质量,但缺乏严格的理由和性能评估.

研究的目的:

描述基于随机步行方法的染色体组织分析方法.
在应用这些方法之前和之后,实证地研究随机走路在识别拓相关域 (TAD) 的性能.
为随机步行方法的参数选择提供指导.

主要方法:

基于随机行走的方法 (RWS和RWR) 的特征分析.
对TAD识别性能进行实证调查.
广泛的分析分析,模拟研究和真实数据应用.

主要成果:

随机步行方法,即使具有最佳参数调,在识别TAD方面也显示出最小的改进.
在应用随机步行方法后,在下游分析中观察到几乎没有增强.
缺乏参数选择的实际指导方针 (例如,RWS的步数,RWR的重启概率) 阻碍了有效的应用.

结论:

该研究建议谨慎使用基于随机走路的方法来改善Hi-C和scHi-C数据质量.
随机步行方法并不能始终提高关键基因组结构的识别,如TADs.
研究人员在使用这些方法进行分析之前,应仔细考虑其有限的益处和缺乏明确的参数指导方针.

关键词:

这就是Hi-C.在拓上相关的域名.随机步行随机步行随机步行随机步行与重新启动.滑滑滑滑滑

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