MCSPACE:从高通量共定位数据中推断微生物群的时空动态.
bioRxiv : the preprint server for biology
|December 16, 2024
概括
我们开发了MCSPACE,这是一款用于分析复杂肠道微生物组数据的AI工具. 它揭示了微生物社区结构和随时间推移的动态,有助于理解宿主-微生物相互作用.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
背景情况:
- 高通量测序使得大规模的微生物组分析成为可能.
- 微生物组共定位数据由于高维度和噪音而存在独特的挑战.
- 了解肠道微生物群的空间结构对于宿主微生物生态系统的功能至关重要.
研究的目的:
- 开发一种概率AI方法 (MCSPACE) 来从同定位数据中推断空间连贯的微生物组合.
- 描述微生物随时间推移的动态和对干扰的反应.
- 为微生物组生物地理研究提供一个开源工具.
主要方法:
- 开发MCSPACE,一种概率AI方法,用于微生物组共定位数据分析.
- 在受到饮食干扰的小鼠中生成大规模纵向微生物群同定位数据集.
- 对MCSPACE与使用小鼠和人类纵向数据集的现有方法进行比较.
主要成果:
- MCSPACE成功地推断出空间连贯的微生物组合及其动态.
- 与现有方法相比,该方法显示出更高的性能.
- 识别人类肠道中暂时持久和动态的微生物组合.
- 揭示了由特定的饮食成分诱导的小鼠肠道微生物组合的变化.
结论:
- MCSPACE是阐明微生物组生物地理和时空结构的强大工具.
- 该方法提供了对宿主微生物生态系统中空间关系的作用的见解.
- 作为一个开源软件工具,MCSPACE可供研究界使用.
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