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与FLORAL相关的高维纵向微生物特征具有时间变化的结果

Teng Fei¹, Victoria Donovan^1,2, Tyler Funnell³

¹Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center.

bioRxiv : the preprint server for biology

|March 3, 2025

概括

这项研究引入了一种新的统计方法,用于分析纵向微生物群数据. 该方法改善了与患者特征相关的微生物生物标记物的识别,增强了微生物组研究中的生物标记物发现.

科学领域:

微生物组研究的研究.
统计建模统计建模
发现生物标志物的发现.

背景情况:

纵向微生物组研究需要方法来将时间依赖的患者数据与微生物样本相关联.
当前的方法通常在所有特征中采用单一的建模方法,然后进行错误发现率 (FDR) 调整.
这可能会限制灵敏度和识别真正关联的能力.

研究的目的:

为分析纵向微生物组数据制定先进的统计策略.
识别与时间变化的患者特征相关的微生物生物标志物.
为了提高微生物组生物标志物发现的灵敏度和FDR控制.

主要方法:

直接建模使用的日志比率处罚一般化估计方程.
处理微生物特征作为高维组成共变量.
用人交叉验证变量和模型选择,包括相关性结构.

主要成果:

与现有的最先进的方法相比,提出的方法显示出更高的灵敏度.
实现了对错误发现率 (FDR) 的强有力的控制.
在癌症患者数据中成功识别了肠道健康指标和相关的微生物标志物.

结论:

这种新方法为纵向微生物组研究中的生物标志物发现提供了增强的能力.
在现实应用中显示出强大的实用性,例如分析饮食摄入量和肠道微生物群.
该方法可通过开源R包FLORAL.提供.

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