研究设计,污染和数据特征对微生物组研究结果和解释的影响
Jose Agudelo1, Aaron W Miller1,2
1Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, Ohio, USA.
mSystems
|August 6, 2025
概括
在低生物质环境中的微生物组研究即使有污染也可靠. 影响结果的关键因素是群体不相似性和独特的种类,而不是污染,特别是在使用内部负控时.
科学领域:
- 微生物组研究的研究.
- 环境微生物学环境微生物学
- 生物信息学是一种生物信息学.
背景情况:
- 高通量测序使得在低生物质环境中的微生物组研究成为可能.
- 污染在低生物质微生物组研究中带来了重大挑战.
- 其余污染对统计结果的影响还未得到充分研究.
研究的目的:
- 量化评估研究设计因素如何影响微生物组分析.
- 评估残留污染对低生物质环境的统计结果的影响.
- 为了比较不同算法的处理污染的性能.
主要方法:
- 利用模拟和现实世界低生物质数据集.
- 分析了样本数量,独特种群和群体不相似性的影响.
- 使用DESeq2和ANCOM-BC算法评估污染对差异丰度分析的影响.
主要成果:
- 群体不相似性和独特类型的数量是统计结果的主要驱动因素.
- 污染对β多样性产生了边际影响,但当存在显著水平时,改变了差异丰富的种群数量.
- 在随机污染的情况下,DESeq2的表现优于ANCOM-BC的表现;在群组加权污染的情况下,算法表现不明确.
- 在差异丰度分析中,假阳性率保持在15%以下.
结论:
- 经过验证的协议与内部负控将残留污染对统计结果的影响降至最低.
- 污染很少影响微生物群差异的检测,但可能会影响差异丰富的种群数量.
- 对于污染评估,内部负控制比公布的污染物清单更可靠.
- 低生物质微生物组研究已经减少了功率,但观察到的差异不太可能是污染驱动的.
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