NMFGOT:一个多视角的学习框架,用于微生物组和代谢组的综合分析,并提供最佳的运输计划
Yuanyuan Ma1,2, Lifang Liu3
1School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, Hubei, China. chonghua_1983@126.com.
NPJ biofilms and microbiomes
|November 24, 2024
概括
NMFGOT是一种用于分析复杂微生物组和代谢组数据的新工具包. 它揭示了在胃癌等疾病中显著的微生物代谢物联系,进步了我们对人类健康的理解.
科学领域:
- 微生物学 微生物学
- 代谢学 代谢学 代谢学
- 生物信息学是一种生物信息学.
背景情况:
- 高通量测序产生了大量的微生物群数据,为疾病机制提供了洞察力.
- 分析微生物,代谢物和人类环境之间的复杂相互作用是具有挑战性的.
- 现有的多学科整合方法与非线性,高阶交互作斗争.
研究的目的:
- 开发一套多功能工具包,用于微生物组和代谢组数据的综合分析.
- 为了解决疾病中微生物-代谢物相互作用的复杂性.
- 通过多主题数据集成,提高对人类复杂疾病的理解.
主要方法:
- 开发了NMFGOT,一种使用非负矩阵因子化与图形规则化的最佳传输的无监督学习框架.
- 利用最佳运输来测量微生物样本之间的概率距离,处理非线性相互作用.
- 整合空间规范化以保持跨数据模式的样本一致性.
主要成果:
- 与现有的多omics集成方法相比,NMFGOT在多个数据集中表现出优异的性能.
- 在胃癌 (GC) 和末期病 (ESRD) 中成功确定了显著和稳定的代谢物-微生物关联.
- 促进下游分析,包括途径丰富和特定疾病的关联分析.
结论:
- NMFGOT为整合微生物组和代谢组数据提供了一个强大的框架.
- 该工具包增强了在复杂疾病中发现关键代谢物-微生物关联的发现.
- NMFGOT有助于更深入地了解人类疾病背后的病因机制.
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