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不浪费,不想要:重新审视了质疑稀化实践的分析
1Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA.
mSphere
|December 6, 2023
概括
稀疏化是一种强大的方法来规范微生物组测序数据,与之前的说法相反. 这项研究证明了稀有化.
科学领域:
- 微生物组研究的研究.
- 生物信息学是一种生物信息学.
- 统计生态学 统计生态学
背景情况:
- 在16S rRNA基因测序数据中对不均的测序深度的规范化对于微生物组分析至关重要.
- 麦克默迪和福尔摩斯在2014年进行的一项广泛引用的研究反对稀释微生物组数据,这显著影响了当前的做法.
- 关于稀释和稀释的定义仍然存在混,这影响了微生物组数据的解释.
研究的目的:
- 通过解决原始模拟研究中的局限性,重新评估稀有化微生物组数据的可接受性.
- 研究特定模拟设计选择对麦克默迪和福尔摩斯得出的结论的影响.
- 确定控制微生物组数据分析中的不均序列化努力的最稳健的方法.
主要方法:
- 再现了McMurdie和Holmes (2014) 的模拟,以验证他们的发现.
- 确定并评估了可能危及原始研究模拟设计和分析的11个因素.
- 扩展了分析以评估不同条件下的各种α和β多样性指标的稀有化.
主要成果:
- 原始研究的模拟设计包含了几个有问题的选择,这些选择对稀释和稀释的性能产生了负面偏见.
- 具体问题包括对生态距离的评估,去除低测序深度样本以及将测序努力与治疗组混.
- 当正确实施和分析时,发现稀释是正常化测序深度的最强大的方法.
结论:
- 与"不可接受"的说法相反,稀有化是一种有价值和强大的工具,用于分析16S rRNA基因测序数据.
- 稀疏化有效控制不均的测序力度,并限制微生物组研究中的错误发现率.
- 这些发现提倡在微生物组数据规范化中适当使用稀有化,纠正以前有影响力的研究中的误解.
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