Description Details Author(s) References

This package provides tools to check whether a vector of p-values respects the assumptions of classical FDR (false discovery rate) control procedures.

It is built to be easily used by non-statisticians in the context of quantitative proteomics (yet, it can be applied in other contexts).

Concretely, it allows estimating the proportion of true null hypotheses (i.e. proportion of non-differentially abundant proteins or peptides in a relative quantification experiment), as well as checking whether the p-values are adequately distributed for further FDR control.

In addition, the package allows performing an adequately chosen adaptive FDR control procedure to get adjusted p-values.

A tutorial giving a practical introduction to this package is available in the supplementary material of Giai Gianetto et al. (2016).

Package: | cp4p |

License: | GPL-3 |

Depends: | multtest, qvalue, limma, MESS, graphics, stats |

This package is composed of three functions that take as input a vector of p-values resulting from multiple two-sided hypothesis testing (such as multiple t-tests for equal means for instance).

First, the function `estim.pi0`

allows determining the proportion of true null hypotheses among the set of tests using eight different estimation methods proposed in literature.

Second, the function `calibration.plot`

proposes an intuitive plot of the p-values, so as to visually assess their behavior and well-calibration.

Third, the function `adjust.p`

allows obtaining adjusted p-values in view to perform an adaptive FDR control from a chosen level.

Two proteomic datasets named `LFQRatio2`

and `LFQRatio25`

allow to use these functions in a concrete framework where the proportion of non-differentially abundant proteins is known.

Quentin Giai Gianetto, Florence Combes, Claire Ramus, Christophe Bruley, Yohann Cout<c3><a9>, Thomas Burger

Maintainer: Quentin Giai Gianetto <[email protected]>

Giai Gianetto, Q., Combes, F., Ramus, C., Bruley, C., Cout<c3><a9>, Y., Burger, T. (2016). Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in quantitative experiments. Proteomics, 16(1), 29-32.

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