scHiCyclePred:一种深度学习框架,用于从单细胞 Hi-C 数据中预测细胞周期阶段,使用多尺度交互信息
Yingfu Wu1,2,3, Zhenqi Shi1, Xiangfei Zhou1
1School of Software, Shandong University, Jinan, Shandong, China.
Communications biology
|July 31, 2024
概括
我们开发了scHiCyclePred,这是一个新的模型,可以使用单细胞Hi-C (scHi-C) 数据准确预测细胞周期阶段. 该工具增强了对3D染色体结构动态的理解,并有助于疾病研究.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 计算生物学 计算生物学
背景情况:
- 单细胞Hi-C (scHi-C) 技术使得在整个细胞周期中研究3D染色质结构成为可能.
- 从scHi-C数据准确预测细胞周期阶段是具有挑战性的.
研究的目的:
- 介绍scHiCyclePred,一种新的预测模型,用于使用scHi-C数据进行细胞周期阶段确定.
- 为了利用多尺度的3D染色体相互作用特征来提高预测准确性.
主要方法:
- scHiCyclePred集成了多个功能集,包括多尺度交互信息,以捕获3D染色体结构.
- 对Nagano_method和CIRCLET进行了比较分析,使用标准指标 (ACC,F1,精度,回忆,BACC).
- 在复杂的组织数据集 (Liu_dataset) 上进行了评估.
主要成果:
- 在预测细胞周期阶段方面,scHiCyclePred显著优于现有的方法,如Nagano_method和CIRCLET.
- 在Liu_dataset上,scHiCyclePred比CIRCLET显著改善: +0.39 (ACC), +0.52 (F1), +0.52 (精度), +0.39 (回忆).
- 这项研究为细胞周期期间的3D染色质动力学和基因特征提供了新的见解.
结论:
- scHiCyclePred提供了一种可靠的方法,可以从scHi-C数据中预测细胞周期阶段.
- 该模型增强了对细胞循环依赖的染色质组织的理解.
- 这些发现对细胞生物学和疾病研究有影响,该工具可在GitHub上找到.
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