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首页> 外文期刊>BMC Medical Genomics >Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease
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Expression-based Pathway Signature Analysis (EPSA): Mining publicly available microarray data for insight into human disease

机译：基于表达的途径签名分析（EPSA）：挖掘公开可用的微阵列数据以深入了解人类疾病

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Background Publicly available data repositories facilitate the sharing of an ever-increasing amount of microarray data. However, these datasets remain highly underutilized. Reutilizing the data could offer insights into questions and diseases entirely distinct from those considered in the original experimental design. Methods We first analyzed microarray datasets derived from known perturbations of specific pathways using the samr package in R to identify specific patterns of change in gene expression. We refer to these pattern of gene expression alteration as a "pathway signatures." We then used Spearman's rank correlation coefficient, a non-parametric measure of correlation, to determine similarities between pathway signatures and disease profiles, and permutation analysis to evaluate false discovery rate. This enabled detection of statistically significant similarity between these pathway signatures and corresponding changes observed in human disease. Finally, we evaluated pathway activation, as indicated by correlation with the pathway signature, as a risk factor for poor prognosis using multiple unrelated, publicly available datasets. Results We have developed a novel method, Expression-based Pathway Signature Analysis (EPSA). We demonstrate that ESPA is a rigorous computational approach for statistically evaluating the degree of similarity between highly disparate sources of microarray expression data. We also show how EPSA can be used in a number of cases to stratify patients with differential disease prognosis. EPSA can be applied to many different types of datasets in spite of different platforms, different experimental designs, and different species. Applying this method can yield new insights into human disease progression. Conclusion EPSA enables the use of publicly available data for an entirely new, translational purpose to enable the identification of potential pathways of dysregulation in human disease, as well as potential leads for therapeutic molecular targets.
机译：背景技术公开可用的数据存储库促进了越来越多的微阵列数据的共享。但是，这些数据集仍然未被充分利用。重新利用数据可以提供与原始实验设计所考虑的问题和疾病完全不同的见解。方法我们首先使用R中的samr软件包分析了源自特定途径已知扰动的微阵列数据集，以鉴定基因表达变化的特定模式。我们将基因表达改变的这些模式称为“途径签名”。然后，我们使用Spearman秩相关系数（一种非参数相关性度量）来确定途径特征和疾病谱之间的相似性，并使用置换分析来评估错误发现率。这使得能够检测出这些途径特征和人类疾病中观察到的相应变化之间的统计学显着相似性。最后，我们评估了通路激活（如与通路特征的相关性所示），这是使用多个不相关，可公开获得的数据集预后不良的危险因素。结果我们开发了一种新颖的方法，基于表达的途径签名分析（EPSA）。我们证明，ESPA是一种严格的计算方法，用于统计评估高度不同的微阵列表达数据源之间的相似程度。我们还展示了EPSA如何在许多情况下用于对疾病预后不同的患者进行分层。尽管具有不同的平台，不同的实验设计和不同的物种，EPSA也可以应用于许多不同类型的数据集。应用这种方法可以对人类疾病的进展产生新的见解。结论EPSA可以将公开可用的数据用于全新的翻译目的，从而可以识别人类疾病中潜在的失调途径，以及治疗性分子靶标的潜在先导。

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