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source("http://bioconductor.org/biocLite.R")
biocLite("msmsTests")
In most cases, you don't need to download the package archive at all.
msmsTests
LC-MS/MS Differential Expression Tests
Bioconductor version: 2.14
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
Maintainer: Josep Gregori i Font <josep.gregori at gmail.com>
Citation (from within R,
enter citation("msmsTests")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("msmsTests")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("msmsTests")
| R Script | msmsTests: controlling batch effects by blocking | |
| R Script | msmsTests: post test filters to improve reproducibility | |
| Reference Manual |
Details
| biocViews | MassSpectrometry, Proteomics, Software |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL-2 |
| Depends | R (>= 3.0.1), MSnbase, msmsEDA |
| Imports | edgeR, qvalue |
| Suggests | |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | msmsTests_1.2.0.tar.gz |
| Windows Binary | msmsTests_1.2.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | msmsTests_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | msmsTests_1.2.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
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