parafac4microbiome:使用并行因子分析对纵向微生物组数据的探索性分析
G R van der Ploeg1, J A Westerhuis1, A Heintz-Buschart1
1Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
mSystems
|May 21, 2025
概括
平行因子分析 (PARAFAC) 有效地分析了纵向微生物群数据,揭示了传统方法错过的时间动态. 这种方法增强了比较研究,并识别了微生物转移,即使缺少数据,并且可以在R包中获得.
科学领域:
- 微生物学 微生物学
- 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:
- 纵向微生物组研究产生复杂的多维数据.
- 传统的方法,如主要组件分析,往往无法有效地捕捉时间动态.
- 多路分析方法为结构化微生物组数据提供了更合适的框架.
研究的目的:
- 为了证明平行因子分析 (PARAFAC) 对于探索纵向微生物组数据的有用性.
- 展示PARAFAC识别时间模式和结构变化的能力.
- 为应用PARAFAC在微生物组研究中提供一个可访问的R包.
主要方法:
- 将并行因子分析 (PARAFAC) 应用于三个不同的纵向微生物群数据集.
- 数据以三向阵列形式组织:受试者,微生物丰度和时间点.
- 为微生物子群体的识别而应用的后期聚类.
主要成果:
- 在一项体外微生物组研究中,PARAFAC成功地确定了初级时间解析变异.
- 该方法区分了受试者群体,并改善了婴儿肠道微生物组纵向研究中的比较分析,即使缺少数据.
- 在口腔微生物组的牙周炎干预研究中,PARAFAC促进了微生物子社区的识别.
结论:
- 平行因子分析 (PARAFAC) 是一种强大而通用的方法,用于在各种环境中分析纵向微生物群数据.
- 该方法有效地捕捉时间和结构模式,优于传统方法.
- 该parafac4microbiome R包为研究人员提供了可访问的工具,以便他们在自己的研究中应用PARAFAC.
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