一个新的强大的网络构建和分析工作流程,用于挖掘婴儿微生物群的关系.
Wei Jiang1, Yue Zhai1, Dongbo Chen1
1Laboratory of Microbiology, Immunology, and Metabolism, Diprobio (Shanghai) Co., Limited, Shanghai, China.
mSystems
|January 2, 2025
概括
一个新的基于概率的联合检测模型 (PBCDM) 可靠地分析婴儿肠道微生物群的发展. 这种方法识别了核心微生物关系,揭示了随着婴儿微生物组的成熟而增加的竞争.
科学领域:
- 微生物组研究的研究.
- 网络分析 网络分析
- 婴儿健康 婴儿健康
背景情况:
- 婴儿肠道微生物群对健康至关重要,其在最初1000天的发展会影响长期结果.
- 了解微生物之间的关系是将微生物群成熟与健康联系起来的关键.
- 分析婴儿微生物群的现有网络方法缺乏强大的评估策略.
研究的目的:
- 开发和验证一种可靠的基于网络的方法来分析婴儿肠道微生物群.
- 评估不同网络构建和评估方法的性能.
- 在婴儿发育过程中识别核心微生物属及其关系.
主要方法:
- 从公开的婴儿微生物组数据集创建了一个测试数据池.
- 通过重复采样评估了四种基于网络的方法.
- 开发并应用了基于概率的联合检测模型 (PBCDM).
- 利用基于透理论的网络剪切策略来识别核心.
主要成果:
- 在网络属性分析中,PBCDM表现出卓越的稳定性和稳定性.
- 用PBCDM构建的网络揭示了不同年龄段的婴儿中的微生物共存模式.
- 确定了核心类网络,显示了相邻年龄组之间的相似性增加.
- 观察到与婴儿微生物组成熟的竞争性微生物关系的增加.
结论:
- PBCDM为构建和评估微生物共存网络提供了一种可靠的方法.
- 基于PBCDM的网络准确地反映了已知的婴儿微生物群特征.
- 该方法为研究微生物关系提供了一个有前途的工具,可以扩展到其他omics数据.
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