Abnormal regulation of signaling pathways is the key causative factor in several diseases. Although many methods have been proposed to identify significantly differential pathways between two conditions via microarray gene expression datasets, most of them concentrate on differences in the pathway components—either the differential expression or the correlation of genes in a given pathway. However, as biological functional units, signaling pathways may have diverse activity patterns across different biological contexts. In order to detect overall changes in pathways, we propose an analysis model called SPAID (Signaling Pathway Analysis based on Information Divergence). SPAID is based on the concept of information divergence, which can be used to compare two conditions by computing the differential probability distribution of the regulation capacity. We compared SPAID with several classical algorithms using different datasets, and the results indicate that SPAID produces higher repeatability, has better performance and universality, and extracts more comprehensive information regarding the underlying biological processes. In conclusion, by introducing the idea of information divergence, our study measures differences in pathways from an overall perspective and will provide a complementary analysis framework for pathway analysis.

中文翻译：

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使用信息分歧从基因表达数据中识别干扰的信号通路

信号通路的异常调节是几种疾病的关键病因。尽管已经提出了许多方法来通过微阵列基因表达数据集，其中大多数集中在途径组成上的差异上—既定途径中基因的差异表达或相关性。但是，作为生物学功能单元，信号通路可能在不同生物学环境中具有多种活动模式。为了检测路径的整体变化，我们提出了一种称为SPAID（基于信息差异的信号路径分析）的分析模型。SPAID基于信息分歧的概念，可通过计算调节能力的差异概率分布来比较两个条件。我们将SPAID与使用不同数据集的几种经典算法进行了比较，结果表明SPAID具有更高的可重复性，更好的性能和通用性，并提取有关潜在生物学过程的更全面的信息。总之，通过引入信息差异的思想，我们的研究从整体角度衡量了途径的差异，并将为途径分析提供补充的分析框架。