Suppr超能文献

MultiTest V.1.2,一个用于二项式组合独立测试并对比例数据与其他相关方法进行性能比较的程序。

MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data.

机构信息

IRD, UMR 177 IRD-CIRAD Trypanosomoses, Centre International de Recherche-Développement sur l'Elevage en zone Subhumide, 01 BP 454 Bobo-Dioulasso 01, Burkina-Faso.

出版信息

BMC Bioinformatics. 2009 Dec 23;10:443. doi: 10.1186/1471-2105-10-443.

Abstract

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 approximately 5% significant tests at alpha = 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.

摘要

背景

当所有的测试都针对同一个假设且方向相同时,合并多个独立的测试一直是几种方法的主题。除了不适当的(在这种情况下)Bonferroni 程序外,Fisher 方法被广泛使用,特别是在群体遗传学中。然而,这种方法受到 SGM(几何平均值周围的对称性)和 Stouffer 的 Z 转换方法的挑战,这些方法对偏 P 值分布的不对称性和非均匀性的敏感性较低。这些不同的方法从未在群体遗传学中目前使用的比例数据上进行比较过。

结果

我们提出了一种新的软件,该软件实现了一种更现代的方法,即广义二项式程序,用于测试观察到的 P 值小于所选阈值的比例与零假设下此类 P 值的预期比例之间的偏差。使用具有标准 P 值分布的模拟数据(分化检验)评估了所有可用程序的性能。所有程序或多或少都与大约 5%的显著检验保持一致,在 alpha = 0.05 下。然后,进行了随着信号强度(克隆繁殖率)增加的连锁不平衡检验,已知这些检验会产生高度非标准的 P 值分布,最后分析了真实的群体遗传学数据。在这些情况下,所有程序似乎或多或少都非常保守,尽管 SGM 似乎稍微保守一些。

结论

基于我们的结果和文献中的讨论,我们得出结论,广义二项式和 Stouffer 的 Z 程序应该优先选择,当测试数量非常少时应该选择 Z。当预期存在强烈的有利于显著结果的发表偏倚会导致 II 型错误膨胀时,更保守的 SGM 可能仍然适用于荟萃分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/12a3/2811122/7dedeb6a3159/1471-2105-10-443-1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验