MuST:用于单细胞空间转录的多态结构转换
Zelin Zang1,2, Liangyu Li1, Yongjie Xu1
1Westlake Institute for Advanced Studies, Westlake University, HangZhou, 310000, China.
Briefings in bioinformatics
|August 28, 2025
概括
空间转录学 (ST) 数据可能会受到主导模式的偏差. 我们开发了多种模式结构转换 (MuST) 来整合多种数据类型, 改善复杂生物系统的组织结构和生物标记分析.
科学领域:
- 基因组学
- 生物信息学
- 计算生物学
背景情况:
- 空间转录学 (ST) 为组织生物学研究提供多模式数据 (转录学,空间,形态).
- ST数据中的模式偏差来自不一致的模式贡献,有利于分析中占主导地位的模式.
- 缓解模式偏差对于ST研究的准确下游分析至关重要.
研究的目的:
- 引入多种模式结构转换 (MuST),一种解决ST数据模式偏差的新方法.
- 将ST数据中的多式联络信息有效地整合到一个统一的隐藏空间中.
- 为ST的各种下游分析任务提供坚实的基础.
主要方法:
- MuST采用拓发现策略和拓融合损失函数.
- 它学习本质的局部结构以解决不同模式之间的不一致性.
- 结合基于拓的和深度学习的技术来实现多式联网数据集成.
主要成果:
- MuST有效地将多模式ST数据集成到一个统一的隐藏空间中.
- 它在识别和保存组织结构和生物标志物方面优于现有的方法.
- 在不同模式的精度和协调方面表现出优势.
结论:
- 使用ST数据分析复杂的生物系统.
- 该方法成功地减轻了模式偏差,提高了下游任务的性能.
- 提供先进的ST数据分析的基础,改善生物洞察力.
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There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...
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