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.
||Poisson based GLM regression|
||The binomial negative of edgeR|
||Table of cumulative frequencies of features by p-values in bins of log fold change|
||Multitest p-value adjustement and post-test filter|
||Volcanplot of the results|
Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori <firstname.lastname@example.org>
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 http://dx.doi.org/10.1016/j.jprot.2013.05.030
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.