This package implements five different versions of the q-value multiple testing procedure proposed by Storey and Tibshirani (2003). The q-value method is based on the false discovery rate (FDR); different versions of the q-value method can be defined depending on the particular estimator used for the proportion of true null hypotheses, \(\pi_0\), which is plugged in the FDR formula. The first version of the q-value uses the \(\pi_0\) estimator in Storey (2002), with tunning parameter \(\lambda\) = 0.5; whereas the second version uses the \(\pi_0\) estimator in Storey and Tibshirani (2003), which is based on an automatic method to select the tunning parameter \(\lambda\). These two methods are only appropriate when the P-values follow a continuous uniform distribution under the global null hypothesis. This package also provides three other versions of the q-value for homogeneous discrete uniform P-values, which often appear in practice. The first discrete version of the q-value uses the \(\pi_0\) estimator proposed in Liang (2016). The second discrete q-value method uses the estimator of \(\pi_0\) proposed in Chen et al. (2014), when simplified for the special case of homogeneous discrete P-values. The third discrete version of the q-value employs a standard procedure but applied on randomized P-values. Once the estimated q-values are computed, the q-value method rejects the null hypotheses whose q-values are less than or equal to the nominal FDR level. All the versions of the q-value method explained above can be seen in Cousido-Rocha et al. (2019).
<U+2018>DQ<U+2019>
This work has received financial support of the Call 2015 Grants for PhD contracts for training of doctors of the Ministry of Economy and Competitiveness, co-financed by the European Social Fund (Ref. BES-2015-074958). The authors acknowledge support from MTM2014-55966-P project, Ministry of Economy and Competitiveness, and MTM2017-89422-P project, Ministry of Economy, Industry and Competitiveness, State Research Agency, and Regional Development Fund, UE. The authors also acknowledge the financial support provided by the SiDOR research group through the grant Competitive Reference Group, 2016-2019 (ED431C 2016/040), funded by the <U+201C>Conseller<U+00ED>a de Cultura, Educaci<U+00F3>n e Ordenaci<U+00F3>n Universitaria. Xunta de Galicia<U+201D>.
Package: <U+2018>DiscreteQvalue<U+2019>
Version: 1.0
Maintainer: Marta Cousido Rocha martacousido@uvigo.es
License: GPL-2
Chen, X., R. W. Doerge, and J. F. Heyse (2014). Methodology Multiple testing with discrete data: proportion of true null hypotheses and two adaptive FDR procedures. arXiv:1410.4274v2.
Cousido-Rocha, M., J. de U<U+00F1>a-<U+00C1>lvarez, and S. D<U+00F6>hler (2019). Multiple testing methods for homogeneous discrete uniform P-values. Preprint.
Liang, K. (2016). False discovery rate estimation for large-scale homogeneous discrete p-values. Biometrics 72, 639-648.
Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64 (3), 479-498.
Storey, J. and R. Tibshirani (2003). Statistical significance for genomewide studies. Proceedings of National Academy of Science 100, 9440-9445.