cp4p-package: Introduction to the CP4P package

Description Details Author(s) References

Description

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).

Details

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.

Author(s)

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

Maintainer: Quentin Giai Gianetto <[email protected]>

References

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.


cp4p documentation built on May 30, 2017, 8:20 a.m.