On the performance of de novo pathway enrichment.
On the performance of de novo pathway enrichment.
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DOI：
10.1038/s41540-017-0007-2
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发表时间：
2017
影响因子：
4
通讯作者：
List M
中科院分区：
文献类型：
--
作者：
Batra R;Alcaraz N;Gitzhofer K;Pauling J;Ditzel HJ;Hellmuth M;Baumbach J;List M
从头途径富集是一种强有力的方法，发现以前未知的分子机制，除了已知的途径。为了实现这一点，特定条件的功能模块提取大型交互网络。在这里，我们给出了一个概述的最先进的，并提出了第一个框架，用于评估现有方法的性能。我们确定了19种工具，并选择了7种代表性的候选工具进行比较分析，进行了超过12，000次运行，涵盖了不同的生物网络，分子谱和参数。我们的研究结果表明，没有一种方法始终优于其他方法。为了减轻生物医学研究人员的这个问题，我们提供了为给定数据集选择适当工具的指南。此外，我们的框架是首次尝试对从头方法进行定量评估，这将使生物信息学界能够客观地比较未来的工具与最先进的方法。从头途径富集方法对于了解疾病的复杂性至关重要。他们可以通过整合分子相互作用网络与表达谱来揭示疾病特异性功能模块。然而，研究人员应该如何从几种方法中选择一种呢？在这篇文章中，来自丹麦和德国的一组科学家首次尝试对现有方法进行定量评估。该框架将帮助生物医学界为其数据找到适当的工具。他们创建了合成金标准和模拟表达谱，对各种工具进行系统评估。他们观察到，相互作用网络的选择、参数设置、表达数据的预处理和表达谱的统计特性在很大程度上影响了结果。结果揭示了各个方法的优势和局限性，并建议使用两种或更多种工具来获得全面的疾病模块。
De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art. De novo pathway enrichment methods are essential to understand disease complexity. They can uncover disease-specific functional modules by integrating molecular interaction networks with expression profiles. However, how should researchers choose one method out of several? In this article, a group of scientists from Denmark and Germany presents the first attempt to quantitatively evaluate existing methods. This framework will help the biomedical community to find the appropriate tool(s) for their data. They created synthetic gold standards and simulated expression profiles to perform a systematic assessment of various tools. They observed that the choice of interaction network, parameter settings, preprocessing of expression data and statistical properties of the expression profiles influence the results to a large extent. The results reveal strengths and limitations of the individual methods and suggest using two or more tools to obtain comprehensive disease-modules.