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MaAsLin 3:完善和扩展用于元原子关联发现的泛化多变量线性模型

William A Nickols^1,2, Thomas Kuntz^1,2, Jiaxian Shen^1,3,4

¹Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

bioRxiv : the preprint server for biology

|December 23, 2024

概括

MaAsLin 3识别了微生物与社区表型的关联,并考虑了数据组成性和复杂设计. 这一新框架提高了准确性,揭示了在微生物组研究中,患病率关联比丰度关联更为常见.

科学领域:

微生物组研究微生物组研究
统计生物信息学是统计的.
计算生物学计算生物学

背景情况:

识别与健康表型等社区属性相关的微生物特征至关重要,但具有挑战性.
微生物分析数据往往表现为稀少和复合,使统计分析复杂化.
现有的模型很难同时控制错误发现率,结合复杂的术语,并评估流行率和丰度关联.

研究的目的:

引入MaAsLin 3 (微生物组多变量关联与线性模型),这是微生物组关联研究的新框架.
为了同时识别复杂微生物组数据集中的丰度和流行关系.
解决数据组合性问题,并扩大微生物组研究中可测试的生物假设类型.

主要方法:

开发了MaAsLin 3,一个微生物组数据的多变量线性建模框架.
使用实验 (spike-in) 或计算技术来计算数据组合性的方法.
将MaAsLin 3应用于合成和真实数据集,包括炎性肠病多omics数据库.

主要成果:

在测试和从组合数据推断关联方面,MaAsLin 3在测试和推断关联方面表现优于最先进的方法.
对炎症性肠病数据集的分析显示,77%的微生物关联是与特征流行率有关,而不是丰富度.
该框架成功证实了以前报告的微生物与炎症性肠道疾病的关联.

结论:

MaAsLin 3提高了微生物组关联识别的准确性和特异性,特别是在复杂的研究设计中.
该框架能够评估流行率和丰度,从而更全面地了解微生物的作用.
这些发现强调了考虑微生物组与现象型关联的特征流行的重要性,特别是在疾病背景下.

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