LC-MS/MS Differential Expression Tests


Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuissance variables. To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition.


Package: msmsTests
Type: Package
Version: 0.99.1
Date: 2013-07-26
License: GPL-2
msms.glm.pois: Poisson based GLM regression
msms.glm.qlll: Quasi-likelihood GLMregression
msms.edgeR: The binomial negative of edgeR Table of cumulative frequencies of features by p-values in bins of log fold change
test.results: Multitest p-value adjustement and post-test filter
res.volcanoplot: Volcanplot of the results


Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori <>


Josep Gregori, Laura Villareal, Alex Sanchez, Jose Baselga, Josep Villanueva (2013). An Effect Size Filter Improves the Reproducibility in Spectral Counting-based Comparative Proteomics. Journal of Proteomics, DOI

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