论文标题
用于共表达数据差异网络分析的伪值回归方法
A Pseudo-Value Regression Approach for Differential Network Analysis of Co-Expression Data
论文作者
论文摘要
差异网络（DN）分析确定了在两个或多个实验条件下基因之间关联度量的变化。在本文中，我们介绍了网络分析（PRANA）的伪值回归方法。这是一种新型的差异网络分析方法，还可以调整其他临床协变量。我们从相互信息（MI）标准开始，然后是伪值计算，然后将其输入到可靠的回归模型中。本文评估了Prana在多变量环境中的模型性能，然后通过各种模拟在单变量和多变量设置中与DNAPATH和DINGO进行比较。评估了差异连接（DC）基因的精度，回忆和F1得分的性能。总体而言，Prana的表现优于Dnapath和Dingo，它们都无法调整可用的协变量，例如患者时代。最后，我们在基因表达综合（GEO）数据库的真实数据应用中采用Prana来识别与慢性阻塞性肺疾病（COPD）相关的直流基因，以证明其效用。据我们所知，这是通过包括附加临床协变量的两个或多个组之间的集体基因表达水平利用回归建模进行DN分析的尝试。总体而言，调整可用协变量可提高DN分析的准确性。
The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a Pseudo-value Regression Approach for Network Analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information (MI) criteria, followed by pseudo-value calculations, which are then entered into a robust regression model. This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus (GEO) database to identify DC genes that are associated with chronic obstructive pulmonary disease (COPD) to demonstrate its utility. To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.
