使用PCA和相关噪声集群的限制
William Lippitt1, Nichole E Carlson1, Jaron Arbet1
1Dept of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
Journal of statistical computation and simulation
|August 23, 2024
概括
在复杂疾病研究中常见的无监督集群方法,通常在杂的相关数据中表现不佳. 这是由于"差异作为相关性"的假设,但新的预处理方法可以帮助改进分析.
科学领域:
- 计算生物学是一种计算生物学.
- 统计遗传学 统计遗传学
- 生物信息学是一种生物信息学.
背景情况:
- 无监督集群广泛用于识别复杂疾病研究中的子组,具有许多特征.
- 现代数据集经常包含高度相关和杂的特征,如SNP,omics数据和电子健康记录.
- 在这些具有挑战性的设置中,集群算法的性能尚未得到充分理解.
研究的目的:
- 对复杂,高维度生物数据的常见无监督聚类方法的实际性能进行评估.
- 确定某些环境中表现不佳的根本原因.
- 提出和评估实际的预处理策略,以提高集群精度.
主要方法:
- 广泛的模拟和实证示例被用来测试各种聚类算法,包括高斯混合模型,k-means变体,VarSelLCM,HDClassifier和Fisher-EM.
- 该研究侧重于具有潜在相关性和噪音特征的数据集.
- 开发和应用了预处理技术,以评估它们对集群性能的影响.
主要成果:
- 包括流行的方法在内的聚类方法在许多测试环境中表现非常差.
- 这种表现不佳的关键驱动因素被确定为"变异为相关性"假设,算法优先考虑高变异特征,忽略低变异特征.
- 开发的预处理方法显示了在特定场景中提高分析性能的潜力.
结论:
- 标准的无监督集群方法对于具有高维度,噪音数据的复杂疾病研究可能不可靠.
- "变异作为相关性"假设是许多当前集群算法的关键限制.
- 提供了关于无监督聚类的实用性和局限性的实用指南,并提供了针对性预处理的潜在好处.
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