Analysis and correction of crosstalk effects in pathway analysis.
Analysis and correction of crosstalk effects in pathway analysis.
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DOI：
10.1101/gr.153551.112
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发表时间：
2013-11
期刊：
影响因子：
7
通讯作者：
Draghici S
中科院分区：
文献类型：
--
作者：
Donato M;Xu Z;Tomoiaga A;Granneman JG;Mackenzie RG;Bao R;Than NG;Westfall PH;Romero R;Draghici S
确定在特定条件下受到显著影响的途径是理解潜在生物现象的关键一步。目前可用于此目的的所有方法都计算P值，旨在量化给定表型中每个途径参与的重要性。这些P值以前被认为是独立的。在这里，我们表明，这是不是这种情况下，许多途径可以通过“串扰”现象大大影响对方的P值。虽然直觉上各种路径可以相互影响，但是这种现象的存在和程度尚未被严格研究，并且最重要的是，目前没有能够量化这种串扰的量的可用技术。在这里，我们表明，所有三个主要类别的途径分析方法（富集分析，功能类评分，和基于拓扑的方法）的串扰现象的严重影响。使用真实的途径和数据，我们表明，在某些情况下，具有显着的P值的途径是没有生物意义的，和一些具有生物意义的途径与非显着的P值成为统计显着时，其他途径的串扰效应被删除。我们描述了一种技术，能够检测，量化和纠正串扰的影响，以及识别独立的功能模块。我们评估了这种新方法的数据，从四个实验涉及三个表型和两个物种。这种方法有望更好地理解个体实验结果，以及更精确地定义特定表型的现有信号通路。
Identifying the pathways that are significantly impacted in a given condition is a crucial step in understanding the underlying biological phenomena. All approaches currently available for this purpose calculate a P-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These P-values were previously thought to be independent. Here we show that this is not the case, and that many pathways can considerably affect each other's P-values through a “crosstalk” phenomenon. Although it is intuitive that various pathways could influence each other, the presence and extent of this phenomenon have not been rigorously studied and, most importantly, there is no currently available technique able to quantify the amount of such crosstalk. Here, we show that all three major categories of pathway analysis methods (enrichment analysis, functional class scoring, and topology-based methods) are severely influenced by crosstalk phenomena. Using real pathways and data, we show that in some cases pathways with significant P-values are not biologically meaningful, and that some biologically meaningful pathways with nonsignificant P-values become statistically significant when the crosstalk effects of other pathways are removed. We describe a technique able to detect, quantify, and correct crosstalk effects, as well as identify independent functional modules. We assessed this novel approach on data from four experiments involving three phenotypes and two species. This method is expected to allow a better understanding of individual experiment results, as well as a more refined definition of the existing signaling pathways for specific phenotypes.
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影响因子：
4.7
作者：
Hanifi-Moghaddam, P.;Boers-Sijmons, B.;Blok, L. J.
通讯作者：
Blok, L. J.
DOI：
10.1152/ajpendo.00010.2005
发表时间：
2005-10-01
影响因子：
5.1
作者：
Li, PP;Zhu, ZX;Granneman, JG
通讯作者：
Granneman, JG
DOI：
10.1152/ajpendo.00009.2005
发表时间：
2005-10-01
影响因子：
5.1
作者：
Granneman, JG;Li, PP;Lu, YY
通讯作者：
Lu, YY
影响因子：
4.4
作者：
Timmons, Brenda C.;Fairhurst, Anna-Marie;Mahendroo, Mala S.
通讯作者：
Mahendroo, Mala S.
影响因子：
1.5
作者：
Uldbjerg, N;Ulmsten, U;Ekman, G
通讯作者：
Ekman, G