msmsTests-package

msmsTests


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.


 htest

 hplot

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.

Josep Gregori

LC-MS/MS Differential Expression Tests

msmsTests-package function

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.

msmsTests-package: LC-MS/MS Differential Expression Tests

Description

Arguments

Details

References