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Abstract：
In molecular biology and genetics, there is a large gap between the ease of data collection and our ability to extract knowledge from these data. Contributing to this gap is the fact that living organisms are complex systems whose emerging phenotypes are the results of multiple complex interactions taking place on various pathways. This demands powerful yet user-friendly pathway analysis tools to translate the now abundant high-throughput data into a better understanding of the underlying biological phenomena. Here we introduce Consensus Pathway Analysis (CPA), a web-based platform that allows researchers to (i) perform pathway analysis using eight established methods (GSEA, GSA, FGSEA, PADOG, Impact Analysis, ORA/Webgestalt, KS-test, Wilcox-test), (ii) perform meta-analysis of multiple datasets, (iii) combine methods and datasets to accurately identify the impacted pathways underlying the studied condition and (iv) interactively explore impacted pathways, and browse relationships between pathways and genes. The platform supports three types of input: (i) a list of differentially expressed genes, (ii) genes and fold changes and (iii) an expression matrix. It also allows users to import data from NCBI GEO. The CPA platform currently supports the analysis of multiple organisms using KEGG and Gene Ontology, and it is freely available at http://cpa.tinnguyen-lab.com.
摘要：
在分子生物学和遗传学中，数据收集的便利性与我们从这些数据中提取知识的能力之间存在很大差距。造成这种差距的事实是，生物体是复杂的系统，其新兴的表型是在各种途径上发生的多种复杂相互作用的结果。这需要强大但用户友好的途径分析工具，以将现在丰富的高通量数据转化为对潜在生物现象的更好理解。在这里，我们介绍共识路径分析(CPA)，一个基于网络的平台，允许研究人员(I)使用八种既定方法(GSEA，GSA,FGSEA,PADOG,影响分析,ORA/Webgestalt,KS测试,Wilcox检验)，(Ii)对多个数据集进行荟萃分析，（iii）结合方法和数据集，以准确地识别受影响的途径潜在的研究条件和（iv）交互探索受影响的途径，浏览通路和基因之间的关系。该平台支持三种类型的输入：（i）差异表达基因列表，(ii)基因和折叠变化和(iii)表达矩阵。它还允许用户从NCBIGEO导入数据。CPA平台目前支持使用KEGG和GeneOntology分析多种生物，它可以在http://cpa上免费获得。tinnguyen-lab.com.
