基于共识权重的基于距离的集群方法的自动校准,使用利的利方法
Barbara Bodinier1, Dragana Vuckovic1, Sabrina Rodrigues1
1Department of Epidemiology and Biostatistics, Imperial College London, Norfolk place, London W2 1PG, United Kingdom.
Bioinformatics (Oxford, England)
|October 17, 2023
概括
具有属性权重的共识集群可以提高集群稳定性和性能. 一个新的"利得分"提高了校准,优于现有的方法,并在基因表达数据中揭示了肺癌亚型.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 数据挖掘 数据挖掘
背景情况:
- 共识集群结合了算法和亚抽样,用于稳定的集群检测.
- 现有的方法表明,共识聚类在性能上超过了原生算法.
研究的目的:
- 扩大共识聚类,以纳入对对距离计算的属性权重.
- 引入一种新的稳定性得分,即"利得分",用于校准集群数量和规范化参数.
- 评估权重共识聚类和尖分数校准的表现.
主要方法:
- 在规范化的共识集群框架内实施了属性权重.
- 开发了从共识聚类输出直接计算的尖得分.
- 进行模拟研究,比较与现有方法的尖分数校准.
- 将增强的共识聚类方法应用于肺组织基因表达数据.
主要成果:
- 与未加权的方法相比,加权的共识聚类显示出更高的性能,特别是与非信息特征相比.
- 使用尖分数的校准比现有的校准分数产生了更好的集群性能.
- 该方法成功地在现实数据中识别了与肺癌亚型相对应的独特集群.
- 利的分数为参数校准提供了一个计算效率高的替代方案.
结论:
- 属性加权和清晰的分数校准显著提高了共识聚类.
- 这种方法为集群分析提供了一个强大的,计算效率高的工具.
- 该方法在识别复杂的生物数据集中的亚型方面具有实际应用,例如肺癌.
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