数据组合性对检测微生物群反应的影响
Brandon Hickman1, Katri Korpela1,2
1Human Microbiome Research Program, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Gut microbes
|December 2, 2025
概括
下一代测序 (NGS) 数据是构成性的,存在分析挑战. 目前对微生物群数据的规范化方法往往无法识别真正的生物信号,导致常见的错误阳性和负性.
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:
- 下一代测序 (NGS) 被广泛用于微生物社区分析.
- 国家统计系统数据的组成性质带来了重大的分析挑战.
- 准确识别微生物信号对于生物学解释至关重要.
研究的目的:
- 对微生物群组成数据的常用规范化方法的有效性进行评估.
- 为了比较相对丰度的表现,CLR,TMM和DESeq2.
- 评估正常化对识别真正的生物信号与人工制造物的影响.
主要方法:
- 模拟的组成微生物群数据被生成.
- 应用了四种规范化方法 (相对丰度,CLR,TMM,DESeq2).
- 通过将规范化数据与绝对丰度数据进行比较,使用差异解释和错误发现率来评估性能.
主要成果:
- 所有测试的规范化方法显示,与绝对丰度数据相比,解释差异减少.
- 正常化方法增加了假阳性和假阴性.
- CLR,TMM和DESeq2的表现并没有超过相对丰度,有时会恶化结果.
结论:
- 当前的规范化方法不能一致地解决NGS数据组合性问题.
- 在组合NGS数据集中,假阳性和假阴性很普遍.
- 建议获得绝对丰度,以区分生物信号与组合元件.
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