李倩莹,蔡云鹏,张凯.基于网络嵌入方法的肠道微生物组大数据网络分析[J].集成技术,2019,8(5):34-48
基于网络嵌入方法的肠道微生物组大数据网络分析
Inferring Gut Microbial Interaction Network from Microbiome DataUsing Network Embedding Algorithm
  
DOI:10.12146/j.issn.2095-3135.20190704001
中文关键词:  生物网络;网络嵌入;聚类;相关性分析
英文关键词:microbial network; network embedding; clustering; correlation analysis
基金项目:国家自然科学基金联合基金项目(U1801265);深圳市经贸委“创新链+产业链”融合专项扶持计划项目(20170502171625936)
作者单位
李倩莹 中国科学院深圳先进技术研究院 深圳 518055;中国科学院大学 北京 100049;健康大数据智能分析技术国家地方联合工程实验室 深圳 518055 
蔡云鹏 中国科学院深圳先进技术研究院 深圳 518055;健康大数据智能分析技术国家地方联合工程实验室 深圳 518055 
张凯 中国科学院深圳先进技术研究院 深圳 518055;健康大数据智能分析技术国家地方联合工程实验室 深圳 518055 
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中文摘要:
      厘清菌群群落与环境的相互关系及其潜在的驱动机理是肠道微生物研究的一项关键任务。通过微生物组高通量测序和大数据分析辨识微生物组分及功能是目前微生物群落分析的主要方法。现有人体肠道微生物的研究主要侧重于描述肠道菌群多样性和组成特征,缺少更深层次的菌群内部互利共生关系及其生态演替的探索。如何由微生物组数据从分子网络角度来研究肠道菌群分布的关联模式是目前亟待解决的问题。该文使用机器学习领域的网络嵌入方法改进传统生物网络结构学习技术过于依 赖节点间的个体相关关系的弊端,更准确地捕捉微生物网络关联的异构性、隐变量和不均衡性等特征。通过对生成的模块与环境变量以及关键代谢物的进行相关性分析,证实了新的网络模块挖掘方法可以更好地提取肠道菌群结构中之前较少被认识到的特征模块,从而更好地评估菌群与菌群之间、菌群与环境之间的制约关系以及菌群代谢功能之间的潜在耦合机制。该研究中描述的方法不仅给肠道微生物群落结构的解析提供了新视角,还可以拓展应用到其他环境微生物领域的研究,通过数据的多阶信息更好地反映群落结构的驱动过程。
英文摘要:
      Identifying the relationship between the gut microbial community and the host environment, as well as the driving mechanism, are the key tasks in gut microbial research. Microbiome high-throughput gene sequencing and big data analysis are currently the mostly used techniques for investigation microbial communities. Existing studies on human gut microbiota data mainly focus on the community diversity and composition, while methods for deep exploration of the ecological and functional relationships among bacteria species are still lacking. An urgent task is therefore raised on developing computational methods to explore the interaction pattern between gut microbial components from in the sense of molecular network from microbiome sequencing data. In this paper, we adopt the network embedding method proposed in machine learning as a remediation to the drawbacks of traditional biological network learning technology which were solely dependent on the direct correlation between nodes, with stronger power in capturing the heterogeneity, hidden variables and imbalance features in microbial network interactions. By analyzing the correlation between the created function modules with the new approach and the environmental variables as well as key metabolite components, it is confirmed that the derived functional modules managed to identify biological-relevant feature that can be less recognized with previous approaches, which are helpful for further modeling of the potential coupling mechanisms among the biological systems. The method described in this article not only provides a new perspective for the analysis of gut microbial community structure, but also can be extended to other environmental microbiology research and reflect the driving process of community structure through multi-level information of data.
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