msmsTests-package: LC-MS/MS Differential Expression Tests
In msmsTests: LC-MS/MS Differential Expression Tests
Description
Details
Author(s)
References
Description
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.
Details
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
pval.by.fc: 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
Author(s)
Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori <josep.gregori@gmail.com>
References
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
msmsTests documentation built on Nov. 8, 2020, 5:25 p.m.
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Description Details Author(s) References
Description
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.
Details
| 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 |
pval.by.fc: | 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 |
Author(s)
Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori <josep.gregori@gmail.com>
References
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
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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