多维缩放改善了微生物组数据的基于距离的聚类
Guanhua Chen1, Xinyue Wang2, Qiang Sun3
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53726, United States.
Bioinformatics (Oxford, England)
|January 28, 2025
概括
使用多维缩放 (MDS) 聚类微生物组数据可以改善患者亚组的发现,特别是当差异微妙时. 这种方法增强了基于距离的聚类,以更好地了解健康和疾病中的微生物.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 统计遗传学 统计遗传学
背景情况:
- 通过微生物组成对患者进行分组对于了解健康和疾病至关重要.
- 基于距离的集群,就像围绕Medoids (PAM) 的分区一样,在计算上是高效的,但在稀疏的微生物信号数据方面却很困难.
研究的目的:
- 提高微生物组数据基于距离的聚类方法的性能.
- 在稀疏信号场景中解决现有方法的局限性.
主要方法:
- 一个两步程序,涉及经典的多维缩放 (MDS) 以减少维度.
- 在从MDS获得的低维数据上应用基于距离的聚类.
主要成果:
- 多维缩放 (MDS) 有效地否定了微生物组数据.
- 与直接基于距离的集群相比,拟议的基于MDS的程序在稀疏信号场景中显著改善了集群性能.
- 该方法的有效性通过模拟和真实世界的微生物群数据应用得到验证.
结论:
- 经典的多维缩放 (MDS) 是微生物组数据集群的强大预处理步骤.
- 拟议的方法为识别与人类健康和疾病相关的微生物模式提供了可靠的解决方案.
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