Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, United States of America.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
PLoS Comput Biol. 2021 May 4;17(5):e1008976. doi: 10.1371/journal.pcbi.1008976. eCollection 2021 May.
Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package "MetaGSCA". It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.
基因集差异共表达分析可以揭示表型和疾病背后的分子机制。然而，概念上相似的个体研究的差异共表达分析通常不一致，并且没有足够的能力提供明确的结果。研究人员可以从一个开源应用程序中受益匪浅，该应用程序可以促进跨研究汇总差异共表达证据，并估计更稳健的共同效应。我们开发了 Meta Gene Set Coexpression Analysis（MetaGSCA），这是一种分析工具，可以通过汇总研究之间的证据来系统地评估先验定义的基因集的差异共表达，从而提供明确的结果。在核心部分，使用一种非参数方法来考虑基因-基因相关结构，以测试基因集在两个比较条件之间是否存在差异共表达，然后为每个单独的研究计算置换检验 p 值。然后进行荟萃分析，将单个研究结果与两种选择之一结合使用：随机截距逻辑回归模型或逆方差方法。我们在两个人类疾病的案例研究中展示了 MetaGSCA，并确定了跨研究与每种疾病高度相关的途径。我们进一步在一个泛癌分析中应用了 MetaGSCA，其中包含 11 种癌症类型中的数百个主要细胞途径。结果表明，大多数途径在泛癌情况下都失调了，其中许多途径在癌症文献中已有报道。我们使用随机生成的基因集进行的分析显示出优异的特异性，表明 MetaGSCA 识别的显著途径/基因集不太可能是假阳性。MetaGSCA 是一个用户友好的工具，以 Web 应用程序和 R 包“MetaGSCA”两种形式实现。它能够对基因集差异共表达数据进行全面荟萃分析，并具有可选的途径串扰网络分析模块，用于识别和可视化具有相似共表达谱的途径。
