GWAS的基因集分析:评估改良Kolmogorov-Smirnov统计的使用。

IF 0.8 4区数学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY

Statistical Applications in Genetics and Molecular Biology Pub Date : 2014-10-01 DOI:10.1515/sagmb-2013-0015

Birgit Debrabant, Mette Soerensen

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引用次数: 3

摘要

我们讨论了改良Kolmogorov-Smirnov (KS)统计在基因集分析中的应用，并回顾了相应的零假设和替代假设。特别是，我们表明，在计算检验统计量时，当增强高度显著基因的影响时，可以考虑相应的检验来推断经典的自包含零假设。我们用仿真的方法估计了不同类型的备选方案的功率，并评估了修正后的KS统计量的权重参数对功率的影响。最后，我们展示了权重参数与基因水平统计的起源和分布之间的类比，并在现实生活中说明了差异权重的影响。

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Gene set analysis for GWAS: assessing the use of modified Kolmogorov-Smirnov statistics.

We discuss the use of modified Kolmogorov-Smirnov (KS) statistics in the context of gene set analysis and review corresponding null and alternative hypotheses. Especially, we show that, when enhancing the impact of highly significant genes in the calculation of the test statistic, the corresponding test can be considered to infer the classical self-contained null hypothesis. We use simulations to estimate the power for different kinds of alternatives, and to assess the impact of the weight parameter of the modified KS statistic on the power. Finally, we show the analogy between the weight parameter and the genesis and distribution of the gene-level statistics, and illustrate the effects of differential weighting in a real-life example.

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来源期刊

Statistical Applications in Genetics and Molecular Biology BIOCHEMISTRY & MOLECULAR BIOLOGY-MATHEMATICAL & COMPUTATIONAL BIOLOGY

自引率

11.10%

发文量

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.