在微生物组样本的差异丰度分析中,分组规范化
Dylan Clark-Boucher1, Brent Coull1, Harrison T Reeder2
1Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States.
ArXiv
|February 12, 2025
概括
这项研究引入了微生物差异丰度分析的新群体智能规范化框架. 新的方法,分组相对日志表达式 (G-RLE) 和折断式总和缩放 (FTSS),提高了统计能力并控制了虚假发现率.
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:
- 微生物样本数量是组成性的,导致偏差差异丰度分析.
- 现有的规范化方法难以保持高方差或构成偏差的错误发现率.
研究的目的:
- 提出一个新的群体级正常化框架,以减少差异丰度分析中的偏差.
- 引入两种新的规范化方法:分组相对逻辑表达式 (G-RLE) 和折断式总和缩放 (FTSS).
主要方法:
- 重新概念化正常化作为一个小组级任务.
- 开发和应用G-RLE和FTSS规范化方法.
- 使用基于模型和合成数据模拟的评估方法.
主要成果:
- 与现有方法相比,G-RLE和FTSS在识别差异丰富的种类中表现出更高的统计能力.
- 提出的方法在具有挑战性的场景中保持了虚假发现率.
- 与MetagenomeSeq相结合的FTSS规范化产生了最佳的性能.
结论:
- 群体智能规范化框架有效减少微生物差异丰度分析中的偏差.
- FTSS规范化是分析微生物组成数据的强大工具.
- 开发的方法为微生物组研究提供了更高的准确性和可靠性.
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