初级方法在微生物差异丰度分析中提供了更可复制的结果.
Juho Pelto1,2, Kari Auranen2,3, Janne V Kujala2
1Department of Computing, University of Turku, University of Turku, 20014, Finland.
Briefings in bioinformatics
|March 26, 2025
概括
微生物组研究中的差异丰度分析 (DAA) 方法在可重复性方面有所不同. 非参数测试和回归模型分析相对丰度,或在存在/缺席数据上的后勤回归,证明了优越的一致性.
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.
- 统计分析 统计分析
背景情况:
- 不同丰度分析 (DAA) 对微生物组研究至关重要.
- 关于最佳的DAA方法存在缺乏共识.
- 可重复性是一个关键的,但往往未被评估的绩效指标.
研究的目的:
- 为了比较14种DAA方法的可复制性.
- 识别DAA方法,在数据集中产生一致的结果.
- 在微生物组研究中指导选择强大的DAA技术.
主要方法:
- 评估了14种DAA方法,使用了53个分类学分析研究的数据 (16S rRNA和shotgun metagenomics).
- 通过检查随机数据集分区和单独研究之间的结果复制来评估方法性能.
- 考虑了相对丰度和存在/缺席数据.
主要成果:
- 在DAA方法中观察到可重现性的显著变化.
- 一些广泛使用的方法产生了不一致的结果.
- 关于相对丰度的非参数方法 (Wilcoxon,顺序回归) 和线性模型 (回归,t测试) 显示出高的一致性.
- 对存在/缺席数据的后勤回归也产生了可比的性能.
结论:
- 可复制性是DAA方法选择的关键基准.
- 对于强大的微生物群DAA,建议分析相对丰度的非参数和线性模型.
- 对存在/缺席数据的后勤回归为DAA提供了一个可行的替代方案.
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