{"title":"基于单变量p值的基因排序优化检测差异表达基因","authors":"H. Noma, S. Matsui","doi":"10.5691/JJB.31.13","DOIUrl":null,"url":null,"abstract":"Ranking significant genes based on the P-value in multiple testing is a simple and common practice in microarray data analysis, and its theoretical optimality is of particular interest. McLachlan et al. (Bioinformatics 2006; 22: 1608-1615) presented a method for calculating the local FDR under normal mixture models and provided a theoretical optimality of the local FDR as a ranking statistic. In this article, we show that the optimal gene ranking based on the local FDR calculated by the McLachlan et al.’s method perfectly accords with that based on P-value under certain conditions. We argue that these conditions are generally satisfied for significant genes with small P-values. We demonstrate it using several real examples.","PeriodicalId":365545,"journal":{"name":"Japanese journal of biometrics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Optimality of Gene Ranking Based on Univariate P-values for Detecting Differentially Expressed Genes\",\"authors\":\"H. Noma, S. Matsui\",\"doi\":\"10.5691/JJB.31.13\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ranking significant genes based on the P-value in multiple testing is a simple and common practice in microarray data analysis, and its theoretical optimality is of particular interest. McLachlan et al. (Bioinformatics 2006; 22: 1608-1615) presented a method for calculating the local FDR under normal mixture models and provided a theoretical optimality of the local FDR as a ranking statistic. In this article, we show that the optimal gene ranking based on the local FDR calculated by the McLachlan et al.’s method perfectly accords with that based on P-value under certain conditions. We argue that these conditions are generally satisfied for significant genes with small P-values. We demonstrate it using several real examples.\",\"PeriodicalId\":365545,\"journal\":{\"name\":\"Japanese journal of biometrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Japanese journal of biometrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5691/JJB.31.13\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Japanese journal of biometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5691/JJB.31.13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
摘要
在微阵列数据分析中,基于多重测试中的p值对重要基因进行排序是一种简单而常见的做法,其理论最优性特别令人感兴趣。McLachlan et al.(生物信息学2006;(22: 1608-1615)提出了一种计算正常混合模型下局部FDR的方法,并给出了局部FDR作为排序统计量的理论最优性。在本文中,我们证明了McLachlan等人基于局部FDR计算的最优基因排序在一定条件下与基于p值的最优排序完全一致。我们认为,对于p值较小的显著性基因,通常满足这些条件。我们用几个真实的例子来证明它。
Optimality of Gene Ranking Based on Univariate P-values for Detecting Differentially Expressed Genes
Ranking significant genes based on the P-value in multiple testing is a simple and common practice in microarray data analysis, and its theoretical optimality is of particular interest. McLachlan et al. (Bioinformatics 2006; 22: 1608-1615) presented a method for calculating the local FDR under normal mixture models and provided a theoretical optimality of the local FDR as a ranking statistic. In this article, we show that the optimal gene ranking based on the local FDR calculated by the McLachlan et al.’s method perfectly accords with that based on P-value under certain conditions. We argue that these conditions are generally satisfied for significant genes with small P-values. We demonstrate it using several real examples.