Assessing Significance in High-Throughput Experiments by Sequential Goodness of Fit and q-Value Estimation

Antonio Carvajal-Rodriguez and Jacobo de Uña-Alvarez

PLOS ONE, 2011, vol. 6, issue 9, 1-8

Abstract: 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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0024700

DOI: 10.1371/journal.pone.0024700

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