A region-based multiple testing method for hypotheses ordered in space or time.

IF 0.8 4区 数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Rosa J Meijer, Thijmen J P Krebs, Jelle J Goeman
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引用次数: 12

Abstract

We present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the R package cherry and is illustrated on a DNA copy number data set.

一种在空间或时间上有序的假设的基于区域的多重检验方法。
我们提出了一种在空间或时间上有序的假设的多重检验方法。在这样的假设下,基本假设和连续假设的区域是有意义的。这些区域假设不仅具有内在意义,而且测试它们还有一个优势,即跨区域的信号(可能很小)可以在一个测试中组合起来。由于潜在感兴趣的区域的预期数量和长度通常事先无法获得,因此我们提出了一种方法,该方法可以在单个多个测试过程中测试所有可能的区域假设以及所有单独的假设,从而控制家族错误率。我们从测试全局零假设开始,当这个假设可以被拒绝时,我们继续进一步指定当前效应的确切位置。该方法在R包cherry中实现,并在DNA拷贝数数据集上进行了说明。
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来源期刊
Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
自引率
11.10%
发文量
8
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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