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首页> 外文期刊>BMC Bioinformatics >MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data
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MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data

机译：MultiTest V.1.2，该程序可将独立测试和性能比较与其他相关方法按比例数据相结合

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Background Combining multiple independent tests, when all test the same hypothesis and in the same direction, has been the subject of several approaches. Besides the inappropriate (in this case) Bonferroni procedure, the Fisher's method has been widely used, in particular in population genetics. This last method has nevertheless been challenged by the SGM (symmetry around the geometric mean) and Stouffer's Z -transformed methods that are less sensitive to asymmetry and deviations from uniformity of the distribution of the partial P -values. Performances of these different procedures were never compared on proportional data such as those currently used in population genetics. Results We present new software that implements a more recent method, the generalised binomial procedure, which tests for the deviation of the observed proportion of P -values lying under a chosen threshold from the expected proportion of such P -values under the null hypothesis. The respective performances of all available procedures were evaluated using simulated data under the null hypothesis with standard P -values distribution (differentiation tests). All procedures more or less behaved consistently with ~5% significant tests at α = 0.05. Then, linkage disequilibrium tests with increasing signal strength (rate of clonal reproduction), known to generate highly non-standard P -value distributions are undertaken and finally real population genetics data are analysed. In these cases, all procedures appear, more or less equally, very conservative, though SGM seems slightly more conservative. Conclusion Based on our results and those discussed in the literature we conclude that the generalised binomial and Stouffer's Z procedures should be preferred and Z when the number of tests is very small. The more conservative SGM might still be appropriate for meta-analyses when a strong publication bias in favour of significant results is expected to inflate type 2 error.
机译：背景技术当所有的测试都对相同的假设和相同的方向进行测试时，将多个独立的测试结合起来已成为几种方法的主题。除了不合适的（在这种情况下）邦费罗尼程序外，费舍尔方法已被广泛使用，尤其是在群体遗传学中。然而，后一种方法一直受到SGM（围绕几何平均值的对称性）和Stouffer的Z转换方法的挑战，这些方法对不对称性和部分P值的分布均匀性的偏差较不敏感。从未在比例数据（例如当前在群体遗传学中使用的比例数据）上比较过这些不同程序的性能。结果我们提供了一种新软件，该软件可以实施更新的方法，即广义二项式程序，该程序测试在零假设下，观察到的处于选定阈值下的P值比例与此类P值的预期比例之间的偏差。在具有标准P值分布的原假设下，使用模拟数据评估了所有可用程序的各自性能（差异测试）。在α= 0.05时，所有程序或多或少都具有〜5％的显着性测试。然后，进行信号强度（克隆繁殖率）提高的连锁不平衡测试，已知该测试会产生高度非标准的P值分布，最后分析了真实的种群遗传数据。在这些情况下，尽管SGM似乎稍微保守一些，但所有过程或多或少都显得非常保守。结论根据我们的结果以及文献中讨论的结果，我们得出结论，当测试次数很少时，应首选广义二项式和Stouffer的Z程序，而Z为首选。当期望显着结果的强烈发表偏见预计会增加2型错误时，较为保守的SGM可能仍适合进行荟萃分析。

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