基于符号和等级的简单和灵活的方法,用于检测微生物组研究中的差异丰度
Leyla Kodalci1, Olivier Thas1,2,3
1Data Science Institute and I-BioStat, Hasselt University, Diepenbeek, Belgium.
PloS one
|September 26, 2023
概括
新的标志和等级转换使微生物组组成数据的有效分析成为可能,克服了零的问题. 这些方法提供了强大的差异丰度测试,性能与现有工具相比.
科学领域:
- 微生物学 微生物学
- 统计分析 统计分析
- 生物信息学是一种生物信息学.
背景情况:
- 来自安普利康序列的微生物组数据是组成性的.
- 通常用于组成数据的逻辑比转换,与微生物组数据集中大量的零数作斗争.
- 现有的方法可能无法充分处理微生物组研究中常见的零通胀.
研究的目的:
- 引入新的统计方法来分析微生物组组成数据.
- 解决微生物组数据集中零值所带来的挑战.
- 为测试差异丰度提供灵活的统计建模框架.
主要方法:
- 将精心挑选的符号和等级转换应用于构成数据.
- 使用逻辑回归和概率指数模型进行统计推理.
- 开发和实施一个名为"signtrans"的R包.
主要成果:
- 符号和等级转换允许对组成数据进行有效的统计推断,即使有许多零.
- 物流回归和概率指数模型为差异丰度测试提供了灵活的框架.
- 一项模拟研究表明,新方法的性能优于大多数现有方法,并且与ANCOM-BC.
结论:
- 新的标志和等级转换方法为分析组成微生物组数据提供了强大的替代方案.
- "signtrans" R包为研究人员提供了可访问的工具,以实施这些先进的统计技术.
- 这些方法提高了在微生物组研究中进行可靠的差异丰度测试的能力.
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