Liu Maidi, Ye Yanqing, Jiang Jiang, Yang Kewei
College of Systems Engineering, National University of Defense Technology, 410073 Changsha, China.
Bioinformatics. 2021 Oct 25;37(20):3569-3578. doi: 10.1093/bioinformatics/btab241.
Modeling microbiome systems as complex networks are known as the problem of network inference. Microbial association network inference is of great significance in applications on clinical diagnosis, disease treatment, pathological analysis, etc. However, most current network inference methods focus on mining strong pairwise associations between microorganisms, which is defective in reflecting the comprehensive interactive patterns participated by multiple microorganisms. It is also possible that the microorganisms involved in the generated network are not dominant in the microbiome due to the mere focus on the strength of pairwise associations. Some scholars tried to mine comprehensive microbial associations by association rule mining methods, but the adopted algorithms are relatively basic and have severe limitations such as low calculation efficiency, lacking the ability of mining negative correlations and high redundancy in results, making it difficult to mine high-quality microbial association rules and accurately infer microbial association networks.
We proposed a microbial association network inference method 'MANIEA' based on the improved Eclat algorithm for mining positive and negative microbial association rules. We also proposed a new method for transforming association rules into microbial association networks, which can effectively demonstrate the co-occurrence and causal correlations in association rules. An experiment was conducted on three authentic microbial abundance datasets to compare the 'MANIEA' with currently popular network inference methods, which demonstrated that the proposed 'MANIEA' show advantages in aspects of correlation forms, computation efficiency, adjustability and network characteristics.
The algorithms and data are available at: https://github.com/MaidiL/MANIEA.
将微生物群落系统建模为复杂网络被称为网络推断问题。微生物关联网络推断在临床诊断、疾病治疗、病理分析等应用中具有重要意义。然而，目前大多数网络推断方法都集中于挖掘微生物之间强烈的成对关联，这在反映多种微生物参与的综合交互模式方面存在缺陷。由于仅关注成对关联的强度，生成网络中涉及的微生物在微生物群落中可能并不占主导地位。一些学者试图通过关联规则挖掘方法来挖掘综合的微生物关联，但所采用的算法相对基础，存在计算效率低、缺乏挖掘负相关能力以及结果冗余度高等严重局限性，使得难以挖掘高质量的微生物关联规则并准确推断微生物关联网络。
我们提出了一种基于改进的Eclat算法的微生物关联网络推断方法“MANIEA”，用于挖掘正负微生物关联规则。我们还提出了一种将关联规则转化为微生物关联网络的新方法，该方法可以有效地展示关联规则中的共现和因果关系。在三个真实的微生物丰度数据集上进行了实验，将“MANIEA”与当前流行的网络推断方法进行比较，结果表明所提出的“MANIEA”在关联形式、计算效率、可调整性和网络特征方面具有优势。
算法和数据可在以下网址获取：https://github.com/MaidiL/MANIEA。
