scUCAF:用于单细胞多组数据集群的不确定性意识的跨组数据对齐和融合网络
Yue Ying1, Nan Wu1, Jinhao Huo1
1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China.
Computational biology and chemistry
|August 30, 2025
概括
我们开发了一个不确定性意识网络, 这种方法通过解决数据质量变化和噪声,增强细胞类型注释和生物标志物发现来改善细胞聚类.
科学领域:
- 计算生物学
- 基因组学
- 生物信息学
背景情况:
- 单细胞多组测序显示了细胞异质性.
- 细胞聚类对于多组数据分析至关重要.
- 现有的方法在数据质量上存在差异,影响特征表示.
研究的目的:
- 提出一个不确定性意识的网络,用于强大的多经济集群.
- 解决单细胞多组数据中的噪声和数据质量变化.
- 为改进下游分析生成可靠的细胞表示.
主要方法:
- 使用负二项式分布的变量自编码器进行噪声强的特征提取.
- 实施高可信度集群引导的对比学习,以实现跨经济学的一致性.
- 开发了一个不确定性意识的融合和门网,用于动态,偏差缓解的omics集成.
主要成果:
- 与现有方法相比,scUCAF在八个单细胞多组数据集上表现出更好的表现.
- 该方法有效地减轻了低质量的数据带来的偏差.
- 通过下游细胞类型注释和生物标志物识别验证了聚类结果.
结论:
- scUCAF提供了可靠的细胞表示,用于多组组.
- 不确定性意识的方法提高了对数据质量变化的稳定性.
- 这种方法在复杂的生物系统,如肝癌中推进了细胞类型注释和生物标志物发现.
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