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

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

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

<p>Abstract</p> <p>Background</p> <p>The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.</p> <p>Results</p> <p>It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.</p> <p>Conclusion</p> <p>Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.</p

Degradative processes over old etched granitic surfaces: dimensional indicators and stage patterns

This paper analyzes the so called, (pilas, gnammas, vasque, etc.), small concavities that retain the water of the rain/ runoff that develops etching processes. Some authors support the beginning of the form already undersoil/ regolith, though all recognize the main development of the form in subaerial environment. The chemical weathering by water increases the size of the pias (pilas, gnamma, vasque), much greater when older is the surface over it develops. Any process that stop the normal evolution (burial, erosion, etc.) can modificate the relative age of the host surface that will be demonstrated younger than in reality is. The size parameters defined by statistic analysis of this measures has a relative chronological value since demonstrate the relationship among evolution of the form and the age of the surface.In this work is presented the analysis of evolution of pias (gnammas, pilas, vasque), of different surfaces developed with a chronology topografically successive to the one which can be assigned also an absolute chronology (by cosmogenic isotopes) that it has been used to know the generation age of the for