基于集群的关联措施与应用程序
Sabyasachi Bera1, Farnaz Fouladi1, Shyamal Peddada1
1Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T.W. Alexander Dr., Durham, 27709, North Carolina, USA.
概括
这项研究介绍了基于Cluster的关联测量 (CLAM),这是一种用于量化复杂数据集中的变量关联的新方法. CLAM有效地识别了隐藏的集群和任意关系,克服了传统关联方法的局限性.
科学领域:
- 生物统计学 生物统计学
- 生物信息学是一种生物信息学.
- 数据科学数据科学数据科学
背景情况:
- 变量关系往往是非线性的,数据集可能包含隐藏的子组.
- 像皮尔森或斯皮尔曼这样的标准相关性指标在这样复杂的场景中可能会误导.
- 在生物医学研究中,具有子结构的高维数据越来越常见.
研究的目的:
- 开发一种新的关联程序,以考虑隐藏的数据集群.
- 量化单变量和多变量变量之间的关联,无论它们的关系形式如何.
- 为生物医学研究中常见的异质数据提供一个强大的衡量标准.
主要方法:
- 开发了基于集群的协会措施 (CLAM),这是一个新的程序.
- 集成的集群算法来检测隐藏的子组.
- 使用了适合检测到集群内的任意关系的关联措施.
主要成果:
- CLAM准确地量化数据中的与隐藏集群的关联.
- 该方法是通用的,适用于单变量和多变量变量.
- 在合成和多样化的现实世界数据集上表现出性能,包括细胞周期基因,微生物组数据和成像数据集.
结论:
- 在复杂,异构的数据集中,CLAM提供了一个强大的协会分析解决方案.
- 该方法解决了传统的相关性测量的局限性,即存在隐藏的子结构.
- CLAM非常适合用于生物医学研究和其他产生高维数据的领域.
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