Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, Vigo, Spain.
PLoS One. 2011;6(9):e24700. doi: 10.1371/journal.pone.0024700. Epub 2011 Sep 9.
We developed a new multiple hypothesis testing adjustment called SGoF+ implemented as a sequential goodness of fit metatest which is a modification of a previous algorithm, SGoF, taking advantage of the information of the distribution of p-values in order to fix the rejection region. The new method uses a discriminant rule based on the maximum distance between the uniform distribution of p-values and the observed one, to set the null for a binomial test. This new approach shows a better power/pFDR ratio than SGoF. In fact SGoF+ automatically sets the threshold leading to the maximum power and the minimum false non-discovery rate inside the SGoF' family of algorithms. Additionally, we suggest combining the information provided by SGoF+ with the estimate of the FDR that has been committed when rejecting a given set of nulls. We study different positive false discovery rate, pFDR, estimation methods to combine q-value estimates jointly with the information provided by the SGoF+ method. Simulations suggest that the combination of SGoF+ metatest with the q-value information is an interesting strategy to deal with multiple testing issues. These techniques are provided in the latest version of the SGoF+ software freely available at http://webs.uvigo.es/acraaj/SGoF.htm.
我们开发了一种新的多重假设检验调整方法，称为 SGoF+，它作为一种顺序拟合优度元检验实现，是先前算法 SGoF 的一种改进，利用了 p 值分布的信息来固定拒绝区域。新方法使用基于 p 值均匀分布与观测值之间最大距离的判别规则，为二项式检验设置零假设。与 SGoF 相比，这种新方法显示出更好的功效/pFDR 比。事实上，SGoF+自动设置阈值，以在 SGoF'算法族中实现最大功效和最小假未发现率。此外，我们建议将 SGoF+提供的信息与拒绝给定的零假设集时所承诺的 FDR 估计结合起来。我们研究了不同的正错误发现率、pFDR 估计方法，以联合 q 值估计与 SGoF+方法提供的信息。模拟表明，SGoF+元检验与 q 值信息的结合是处理多重检验问题的一种有趣策略。这些技术在最新版本的 SGoF+软件中提供，可在 http://webs.uvigo.es/acraaj/SGoF.htm 免费获得。
