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微生物组测序数据的强大的差异丰富性分析
Guanxun Li1, Lu Yang2, Jun Chen2
1Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
Genes
|November 25, 2023
概括
微生物组数据通常包含异常值和重尾,这可能会降低差异丰度分析 (DAA) 的功率. 强大的休伯回归有效地减轻了这些问题,改善了DAA结果.
科学领域:
- 微生物学 微生物学
- 统计生物信息学是统计的.
背景情况:
- 微生物组数据经常显示异常值和重尾分布.
- 这些数据特征对差异丰度分析 (DAA) 的影响仍未得到充分研究.
研究的目的:
- 通过使用差异丰度分析线性模型 (LinDA) 方法,系统地调查异常值和重尾性对DAA的影响.
- 提出和评估减轻这些数据挑战的策略.
主要方法:
- 检查了异常值和重尾性对LinDA的影响.
- 作为一个预处理步骤,研究了数据的获胜化.
- 概括了LinDA以整合强大的回归技术,特别是Huber回归.
主要成果:
- 异常值和重尾性显著降低了标准LinDA的统计能力.
- 温索化数据显示了一些改善.
- 在一般化的LinDA框架内,强大的Huber回归在解决异常值和重尾性方面表现出卓越的表现.
结论:
- 像LinDA这样的标准DAA方法对异常值和重尾微生物组数据敏感.
- 强大的休伯回归提供了一个有效的解决方案,以提高DAA的可靠性,在存在这样的数据特征.
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