ADAPT:通过汇集托比特模型来分析微生物群差异丰富性
Mukai Wang1, Simon Fontaine2, Hui Jiang1
1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, Michigan, 48109, United States.
Bioinformatics (Oxford, England)
|November 7, 2024
概括
我们开发了ADAPT,这是微生物组差异丰度分析 (DAA) 的新方法. ADAPT有效处理零计数和组成数据,提高识别微生物差异的准确性.
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.
- 统计建模 统计建模
背景情况:
- 微生物组数据的差异丰度分析 (DAA) 由于过度的零和数据组成性而具有挑战性.
- 现有的DAA方法经常做出毫无根据的假设或使用复杂的模型来解决这些问题.
研究的目的:
- 引入一种新的方法,ADAPT (通过汇集托比特模型分析微生物群差异丰富性),旨在克服微生物群DAA中的挑战.
- 为识别差异丰富的微生物种群提供一个强大的统计框架.
主要方法:
- ADAPT将零计数视为左边审查的观测,避免复杂的建模.
- 它采用了理论上合理的方法来选择非差异丰富的种类作为参考.
- 该方法以R包的形式实现,可在Bioconductor和GitHub上使用.
主要成果:
- 模拟表明,与现有方法相比,ADAPT提供了优越的错误发现率控制和更高的统计能力.
- 从COHRA2研究中应用到16S rRNA和猎枪元基因组学数据,揭示了新的见解.
- 对婴儿口腔微生物组数据的分析确定了与儿童早期牙损伤的关联.
结论:
- ADAPT为微生物群差异丰度分析提供了强大而准确的解决方案.
- 该方法增强了对微生物社区结构及其与健康结果的关系的理解.
- 在微生物组研究中,ADAPT促进了更可靠的发现,特别是在复杂的数据集中.
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