实验偏差对微生物组数据组合分析的影响
Yingtian Hu1, Glen A Satten2, Yi-Juan Hu1
1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
Genes
|September 28, 2023
概括
LOCOM是一种新的方法,可以解释微生物组数据分析中的实验偏差. 它仍然具有强大的偏见,在控制虚假发现率和保持灵敏度方面表现优于其他方法.
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:
- 微生物组数据分析容易受到来自DNA提取和PCR放大的实验偏差的影响.
- 现有的统计方法往往忽略了这些偏差,可能会影响结果.
- 之前的模型解决了主要效应偏差,但没有纳税种-纳税种相互作用.
研究的目的:
- 制定微生物组数据中相互作用偏差的模型.
- 评估相互作用偏差对差异丰度测试方法的影响.
- 评估LOCOM方法与其他组合分析技术的性能.
主要方法:
- 开发了一个模型来描述微生物组数据中的相互作用偏差.
- 用基于交互偏差模型的模拟进行评估.
- 使用模拟数据,将LOCOM的性能与其他现有方法进行了比较.
主要成果:
- 洛科姆显示出对合理范围的相互作用偏差的稳定性.
- 其他方法显示虚假发现率 (FDR) 膨胀,即使有主要效应偏差.
- 在其他方法失败的情况下,LOCOM保持了更高的灵敏度,并有效控制了FDR.
结论:
- LOCOM是第一个在微生物组分析中解释实验偏差的方法,并且对主要效应偏差具有稳定性.
- 在构成分析中,LOCOM的表现优于其他考虑的方法,特别是在相互作用偏差条件下.
- 开发的方法为微生物组数据的解释提供了更高的可靠性.
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