NetSHy: network summarization via a hybrid approach leveraging topological properties.
NetSHy: network summarization via a hybrid approach leveraging topological properties.
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DOI：
10.1093/bioinformatics/btac818
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发表时间：
2023-01-01
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生物网络可以提供对底层过程的系统级理解。在许多情况下，网络具有高度的模块化，即它们由节点子集（通常称为子网或模块）组成，它们高度互连并且可以执行单独的功能。为了进行后续分析以调查已识别模块与感兴趣变量之间的关联，通常需要最好地解释模块信息并降低维度的模块摘要。用于获取网络表示的传统方法通常仅依赖于网络内节点的配置文件，而忽略固有的网络拓扑信息。在本文中，我们提出了 NetSHy，这是一种混合方法，能够减少网络维度，同时结合拓扑属性来帮助解释下游分析。特别是，NetSHy 对节点配置文件和直接从网络相似性矩阵导出的众所周知的拉普拉斯矩阵的组合应用主成分分析 (PCA)，以提取主题级别的摘要。基于不同网络规模和稀疏程度的随机和经验网络的模拟场景表明，在恢复与感兴趣表型的真实相关性以及在网络相对稀疏时保持数据中大量可解释的变化方面，NetSHy 优于直接应用于节点配置文件的传统 PCA 方法。随着样本量的减少，与观察到的表型的相关性更加一致，这也证明了 NetSHy 的稳健性。最后，作为下游分析的应用进行全基因组关联研究，其中生物网络上的 NetSHy 总结分数识别出比传统网络表示更显着的单核苷酸多态性。 NetSHy 的 R 代码实现可在 https://github.com/thaovu1/NetSHy 上获取。补充数据可在 Bioinformatics 在线获取。
Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module’s information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy Supplementary data are available at Bioinformatics online.
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DOI：
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发表时间：
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