scMSDA:用于单细胞RNA-seq数据集群的新型多视图融合框架,具有语义和分布对齐
Congcong Jiang1, Wenlan Chen2, Yanyan Tan1
1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
Interdisciplinary sciences, computational life sciences
|February 13, 2026
概括
我们介绍了scMSDA,这是一个用于单细胞RNA测序 (scRNA-seq) 数据聚类的新型多视图框架. 它通过利用语义一致性和分布对齐来改善分析,以实现强大的细胞表示.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 单细胞RNA测序 (scRNA-seq) 为细胞异质性提供了高分辨率,但面临着分析挑战.
- 现有的集群方法往往忽视了局部数据结构,影响了语义关系的捕获.
- 技术噪音和scRNA-seq数据的高维度使得下游分析更加复杂.
研究的目的:
- 开发一个新的多视图融合框架,scMSDA,用于增强scRNA-seq数据集群.
- 通过强制执行语义一致性和分布对齐来学习强大的细胞表示.
- 为了提高scRNA-seq数据集群的准确性和可靠性,以获得生物学见解.
主要方法:
- scMSDA采用数据增强,通过掉队规范化和全球特征聚合.
- 一个远程引导的自适应-消极对比学习策略动态调整负样本贡献.
- 代的中心点精细化和基于最佳运输 (OT) 的交叉视图对齐强制执行分布对齐和集群分离.
主要成果:
- 在17个公开的scRNA-seq数据集中,scMSDA表现出卓越的性能.
- 拟议的方法优于基于多个指标的10种基线聚类方法.
- 实验结果验证了scMSDA在学习scRNA-seq数据的可靠表示形式方面的有效性.
结论:
- scMSDA为scRNA-seq数据集群提供了一个有效的多视图融合框架.
- 该方法成功地解决了scRNA-seq分析中稀疏性,维度和噪声的挑战.
- scMSDA为了解细胞异质性提供了计算生物学方面的重大进步.
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