Multiple hypothesis testing in proteomics: A strategy for experimental work.
Multiple hypothesis testing in proteomics: A strategy for experimental work.
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蛋白质组学中的多重假设检验：实验工作策略。
DOI：
10.1074/mcp.o110.004374
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
D. Skibinski
中科院分区：
文献类型：
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作者：
A. P. Diz;A. Carvajal;D. Skibinski
在定量蛋白质组学工作中，常规检查许多单独蛋白质的表达差异以测试处理之间的显著差异。这导致了多重假设检验问题：当进行许多单独的检验时，许多检验偶然会有显著性，并且是假阳性结果。处理这个问题的统计方法，如错误发现率（FDR）方法，已经传播了十多年。然而，蛋白质组学期刊的调查表明，这种测试并没有广泛实施的一种常用的技术，定量蛋白质组学使用二维电泳（2-DE）。我们概述了多种假设检验方法的选择，包括一些众所周知的和一些鲜为人知的，并提出了一个简单的策略，供实验科学家在定量蛋白质组学工作中使用。该战略的重点是同时使用几种不同方法的可取性，选择和重点取决于研究的优先事项和手头的结果。这种方法被证明使用的情况下，实验和模拟模型数据。
In quantitative proteomics work, the differences in expression of many separate proteins are routinely examined to test for significant differences between treatments. This leads to the multiple hypothesis testing problem: when many separate tests are performed many will be significant by chance and be false positive results. Statistical methods such as the false discovery rate (FDR) method that deal with this problem have been disseminated for more than one decade. However a survey of proteomics journals shows that such tests are not widely implemented in one commonly used technique, quantitative proteomics using two-dimensional electrophoresis (2-DE). We outline a selection of multiple hypothesis testing methods, including some that are well known and some lesser known, and present a simple strategy for their use by the experimental scientist in quantitative proteomics work generally. The strategy focuses on the desirability of simultaneous use of several different methods, the choice and emphasis dependent on research priorities and the results in hand. This approach is demonstrated using case scenarios with experimental and simulated model data.