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Jaehong Yu¹, Md Mozaffar Hosain², Taesung Park^3,4

¹Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Republic of Korea.

Genes & genomics

|September 16, 2025

概括

新的统计方法通过考虑有序的疾病阶段来改善微生物组分析. 基于比例概率模型的变换测试 (POMp) 显示出在复杂疾病中识别微生物生物标志物的巨大潜力.

科学领域:

微生物组研究的研究.
统计生物信息学统计生物信息学
计算生物学是一种计算生物学.

背景情况:

与有序表型相关的微生物群标志物对于了解疾病进展和精准医学至关重要.
由于二进制比较,当前的差异丰度方法往往无法捕捉有序类别的趋势.

研究的目的:

开发和评估明确使用顺序表型结构的微生物组关联分析的统计方法.
解决微生物组数据的稀缺性和零通胀等挑战.
增强检测微生物与有序表型的关联.

主要方法:

提出了三种新的方法:二进制最佳测试,线性趋势测试和基于比例赔率模型的变换测试 (POMp).
方法考虑顺序表型结构,并使用基于 permutation 的 inference 进行稀疏性和零通胀.
对三个肠道微生物组数据集 (肥胖和结直肠癌) 应用方法.

主要成果:

所有提出的方法都确定了差异丰富特征 (DAFs),比现有方法有更强的顺序关联.
基于比例赔率模型的变换测试 (POMp) 在与表型顺序的相关性方面表现出卓越的性能.
使用新方法确定了潜在的生物相关微生物标记物.

结论:

整合顺序信息对于推进微生物组研究至关重要.
开发的统计工具为复杂疾病中的微生物生物标志物发现提供了强大的方法.
这些发现支持使用POMp来识别临床相关的微生物群签名.

关键词:

协会分析协会分析不同的丰度差异.常规的表型常规的表型变试验变试验变试验比例赔率模型的比例赔率模型.

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