assessing significance in high-throughput experiments by sequential goodness of fit and q-value estimation评估意义的高通量实验序列拟合优度和核反应能量估计.pdf

Assessing Significance in High-Throughput Experiments by Sequential Goodness of Fit and q-Value Estimation 1 ˜ 2 Antonio Carvajal-Rodriguez *, Jacobo de Una-Alvarez ´ ´ ´ ´ ´ ´ 1 Departamento de Bioquımica, Genetica e Inmunologıa, Facultad de Biologıa, Universidad de Vigo, Vigo, Spain, 2 Departamento de Estadıstica e Investigacion Operativa, ´ Facultad de Ciencias Economicas y Empresariales, Universidad de Vigo, Vigo, Spain 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 pr