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在肠道微生物组数据中测试潜在类,使用一般化的波松回归模型

Xinhui Qiao¹, Hua He², Liuquan Sun³

¹School of Statistics, University of International Business and Economics, Beijing, China.

Statistics in medicine

|November 3, 2023

概括

这项研究引入了一种新的统计测试,用于在人类微生物组数据中找到隐藏的组. 这种方法有助于分析复杂的微生物社区数据,改善我们对健康和疾病的理解.

科学领域:

微生物组研究微生物组研究
统计建模统计建模
人类健康人类健康人类健康

背景情况:

人类微生物组研究对于了解健康和疾病至关重要.
微生物组数据 (例如,操作分类学单位计数) 通常表现出过度分散和零通货膨胀.
现有的模型,如通用波桑模型和零膨胀的通用波桑模型,解决了这些数据挑战.

研究的目的:

引入一种新的统计测试方法来检测一般化的波桑回归模型中的隐性类.
解决微生物组研究中由于人口异质性而产生的零通货膨胀问题.
为分析复杂的人类微生物组数据提供强大的方法.

主要方法:

开发用于隐性类检测的封闭形式测试统计.
使用估计方程,推断测试统计数据的非对称分布.
广泛的模拟研究来评估方法的性能.
将测试应用于来自Bogalusa心脏研究的真实世界人类肠道微生物组数据.

主要成果:

新的测试方法有效地识别了一般化的波桑回归模型中的潜在类.
模拟研究证明了拟议的测试统计数据的有效性和稳定性.
该方法成功地检测到人类肠道微生物群数据中的潜在类.

结论:

开发的统计测试提供了一个强大的工具,用于揭示微生物组数据中隐藏的结构.
这种方法增强了零膨胀和过度分散的微生物群数据集的分析.
这些发现有助于通过微生物组研究更好地了解人类健康和疾病.

关键词:

博加卢萨心脏研究一般化的Poisson模型隐藏类隐藏类隐藏类微生物组数据的数据运营的分类学单位.零膨胀的一般化Poisson模型

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