{"title":"PUPPI: A pathway analysis method using protein-protein interaction network for case-control data","authors":"R. Chung","doi":"10.1109/CIBCB.2013.6595415","DOIUrl":null,"url":null,"abstract":"The development of statistical pathway analysis methods has focused on testing individual main effects of genes in a pathway on disease. However, gene-gene interactions can also play an important role in complex disease etiology. We developed a pathway analysis method based on a protein-protein interaction network to account for gene-gene interactions in a pathway. We used simulations to evaluate the type I error and power for the method. Our simulation results suggest that the method has correct type I error rates, and can be powerful in the identification of the effects of gene-gene interaction in pathways under different scenarios. The method has been implemented into an efficient software package with C++.","PeriodicalId":350407,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBCB.2013.6595415","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The development of statistical pathway analysis methods has focused on testing individual main effects of genes in a pathway on disease. However, gene-gene interactions can also play an important role in complex disease etiology. We developed a pathway analysis method based on a protein-protein interaction network to account for gene-gene interactions in a pathway. We used simulations to evaluate the type I error and power for the method. Our simulation results suggest that the method has correct type I error rates, and can be powerful in the identification of the effects of gene-gene interaction in pathways under different scenarios. The method has been implemented into an efficient software package with C++.