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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Hypergraph Clustering Based on Intra-class Scatter Matrix for Mining Higher-order Microbial Module
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Hypergraph Clustering Based on Intra-class Scatter Matrix for Mining Higher-order Microbial Module

机译：基于类内散布矩阵的超图聚类挖掘高阶微生物模块

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摘要

Microbial ecosystems are complex, by analyzing co-occurrence modules of microbial communities, we can better understand the conditions of microbial interactions in each environment, and help understand the interaction patterns that maintain the stability of microbial communities. Imbalances in human microbiome are closely related to human disease. Previous modular clustering analysis was based only on the relationship between paired microorganisms. In this paper, we propose calculating the logical relationship between microbial triplet in human body by information entropy and construct a hypergraph based on the triplet network. Based on the hypergraph clustering, we proposed a novel hypergraph clustering algorithm based on intra-class scatter matrix (HCIS) to reconstruct hyperedge similarity, and selected the optimal cluster number by maximizing modularity to analyze higher-order module of microorganisms. The clustering results verify the effectiveness and feasibility of HCIS algorithm for higher-order microbial module analysis.
机译：微生物生态系统很复杂，通过分析微生物群落的共生模块，我们可以更好地了解每种环境中微生物相互作用的条件，并有助于理解维持微生物群落稳定性的相互作用模式。人类微生物组的失衡与人类疾病密切相关。以前的模块化聚类分析仅基于配对微生物之间的关系。本文提出了利用信息熵计算人体微生物三联体之间的逻辑关系，并基于三联体网络构建超图。在超图聚类的基础上，提出了一种基于类内散射矩阵（HCIS）的超图聚类算法，用于重建超边缘相似度，并通过最大化模块化来选择最优的聚类数，以分析微生物的高阶模块。聚类结果验证了HCIS算法在高阶微生物模块分析中的有效性和可行性。

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