A novel signaling pathway impact analysis
A novel signaling pathway impact analysis
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DOI：
10.1093/bioinformatics/btn577
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发表时间：
2009-01-01
期刊：
影响因子：
5.8
通讯作者：
Romero, Roberto
中科院分区：
文献类型：
--
作者：
Tarca, Adi Laurentiu;Draghici, Sorin;Romero, Roberto
动机：基因表达类别比较研究可以鉴定样品组之间的数百或数千个基因差异表达（DE）。从这些实验的结果中获得生物学见解可以通过识别受观察到的变化影响的信号通路来实现。大多数现有的途径分析方法集中于在给定途径中观察到的DE基因的数量（富集分析方法），或者集中于途径基因与样品类别之间的相关性（功能类别评分方法）。这两种方法处理的途径作为简单的基因集，无视复杂的基因相互作用，这些途径建立describ.Results：我们描述了一种新的信号通路的影响分析（SPIA），结合从经典的富集分析与一种新的证据类型，它测量的实际扰动给定的通路在给定的条件下，从证据。自举程序被用来评估所观察到的总路径扰动的意义。使用模拟，我们表明，来自扰动的证据是独立的路径富集的证据。这允许我们计算全局路径显著性P值，其结合富集和扰动P值。我们说明了四个真实的数据集上的新方法的能力。这些数据的结果表明，SPIA具有更好的特异性和更高的灵敏度比几个广泛使用的途径分析方法。
Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed ( DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.Results: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods.