A comparative study of topology-based pathway enrichment analysis methods
A comparative study of topology-based pathway enrichment analysis methods
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DOI：
10.1186/s12859-019-3146-1
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发表时间：
2019-11-04
影响因子：
3
通讯作者：
Michailidis, George
中科院分区：
文献类型：
--
作者：
Ma, Jing;Shojaie, Ali;Michailidis, George
背景途径富集广泛用于组学数据分析，以获得对预定义的基因、蛋白质和代谢物子集的功能作用的生物学见解。在文献中已经提出了大量的方法来完成这项任务。这些方法中的绝大多数使用所研究的生物分子的表达水平以及它们在感兴趣的途径中的成员资格作为输入。最新一代的途径富集方法还利用了关于潜在途径的拓扑结构的信息，正如其评估所揭示的证据，这导致了灵敏度和特异性的提高。然而，仍然缺乏对这些方法的系统的经验比较，使得选择最适合特定实验环境的方法具有挑战性。本比较研究的九个基于网络的方法的途径富集分析的目的是提供一个系统的性能评估的基础上，三个真实的数据集与不同数量的功能（基因/代谢物）和样本数。结果研究结果突出了九种方法的方法和经验差异。特别地，某些方法由于跨表达水平和途径成员的相互连接强度两者的差异而评估途径富集，而其他方法仅利用差异表达水平。在涉及代谢组学数据集的更具挑战性的设置中，结果表明，利用两条信息的方法（NetGSA是一个原型）在检测途径富集方面表现出上级统计能力。结论分析表明，许多方法在测试大尺寸通路时表现同样出色，这是基因组数据的情况。另一方面，考虑到通路中生物分子的差异表达以及拓扑结构变化的NetGSA在测试小尺寸通路时表现出上级性能，这通常是代谢组学数据的情况。
Background Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. Results The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. Conclusion The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.