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.
|Author||Josep Gregori, Alex Sanchez, and Josep Villanueva|
|Date of publication||None|
|Maintainer||Josep Gregori i Font <email@example.com>|
msms.edgeR: Spectral counts differential expression by edgeR
msms.glm.pois: Spectral counts differential expression by Poisson GLM
msms.glm.qlll: Spectral counts differential expression by quasi-likelihood...
msms.spk: Yeast lisate samples spiked with human proteins
msmsTests-package: LC-MS/MS Differential Expression Tests
pval.by.fc: Table of cumulative frequencies of p-values by log fold...
test.results: Multitest p-value adjustment and post-test filter