{"title":"网络差分连接性分析。","authors":"Sen Zhao, Ali Shojaie","doi":"10.1214/21-aoas1581","DOIUrl":null,"url":null,"abstract":"<p><p>Identifying differences in networks has become a canonical problem in many biological applications. Existing methods try to accomplish this goal by either directly comparing the estimated structures of two networks, or testing the null hypothesis that the covariance or inverse covariance matrices in two populations are identical. However, estimation approaches do not provide measures of uncertainty, e.g., <i>p</i>-values, whereas existing testing approaches could lead to misleading results, as we illustrate in this paper. To address these shortcomings, we propose a <i>qualitative</i> hypothesis testing framework, which tests whether the connectivity <i>structures</i> in the two networks are the same. our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing approach could test such hypotheses and provide corresponding measures of uncertainty. Theoretically, we show that under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hypothesis. Empirically, we demonstrate the performance of our proposal using simulation studies and applications in cancer genomics.</p>","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569671/pdf/","citationCount":"0","resultStr":"{\"title\":\"NETWORK DIFFERENTIAL CONNECTIVITY ANALYSIS.\",\"authors\":\"Sen Zhao, Ali Shojaie\",\"doi\":\"10.1214/21-aoas1581\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Identifying differences in networks has become a canonical problem in many biological applications. Existing methods try to accomplish this goal by either directly comparing the estimated structures of two networks, or testing the null hypothesis that the covariance or inverse covariance matrices in two populations are identical. However, estimation approaches do not provide measures of uncertainty, e.g., <i>p</i>-values, whereas existing testing approaches could lead to misleading results, as we illustrate in this paper. To address these shortcomings, we propose a <i>qualitative</i> hypothesis testing framework, which tests whether the connectivity <i>structures</i> in the two networks are the same. our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing approach could test such hypotheses and provide corresponding measures of uncertainty. Theoretically, we show that under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hypothesis. Empirically, we demonstrate the performance of our proposal using simulation studies and applications in cancer genomics.</p>\",\"PeriodicalId\":50772,\"journal\":{\"name\":\"Annals of Applied Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569671/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Applied Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1214/21-aoas1581\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/9/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Applied Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/21-aoas1581","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/9/26 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Identifying differences in networks has become a canonical problem in many biological applications. Existing methods try to accomplish this goal by either directly comparing the estimated structures of two networks, or testing the null hypothesis that the covariance or inverse covariance matrices in two populations are identical. However, estimation approaches do not provide measures of uncertainty, e.g., p-values, whereas existing testing approaches could lead to misleading results, as we illustrate in this paper. To address these shortcomings, we propose a qualitative hypothesis testing framework, which tests whether the connectivity structures in the two networks are the same. our framework is especially appropriate if the goal is to identify nodes or edges that are differentially connected. No existing approach could test such hypotheses and provide corresponding measures of uncertainty. Theoretically, we show that under appropriate conditions, our proposal correctly controls the type-I error rate in testing the qualitative hypothesis. Empirically, we demonstrate the performance of our proposal using simulation studies and applications in cancer genomics.
期刊介绍:
Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.