msmsTests (version 1.10.0)

msms.edgeR: Spectral counts differential expression by edgeR

Description

Given a null and an alternative model, with a two level treatment factor as the two conditions to compare, executes the negative binomial test by edgeR functions to discover differentially expressed proteins between the two conditions. The null and alternative models may include blocking factors.The reference level of the main factor is considered to be the control condition

Usage

msms.edgeR(msnset,form1,form0,facs=NULL,div=NULL,fnm=NULL)

Arguments

msnset

A MSnSet object with spectral counts in the expression matrix.

form1

The alternative hypothesis model as an standard R formula, with the treatment factor of interest, and eventual blocking factors.

form0

The null hypothesis model as an standard R formula.It may be the standard null model (y~.) or contain one or multiple blocking factors.

facs

NULL or a data frame with the factors in its columns.

div

NULL or a vector with the divisors used to compute the offsets.

fnm

NULL or a character string with the treatment factor name, as used in the column names of the factors data frame, and in the formula.

Value

estimated log fold changes, likelihood ratio statistic and corresponding p-value as obtaimed from a call to glmLRT() from the edgeR package.

Details

The right hand site of the formulas is expected to be "y~", with the combination of factors after the tilde. If facs is NULL the factors are taken as default from pData(msnset). If div is NULL all divisors are taken equal to one. If fnm is NULL it is taken to be the first factor in facs.

References

Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881-2887

Robinson MD and Smyth GK (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics, 9, 321-332 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

See Also

MSnSet, edgeR, glmLRT, msmsEDA

Examples

## Example
library(msmsTests)
data(msms.dataset)
e <- pp.msms.data(msms.dataset)
e

null.f <- "y~batch"
alt.f <- "y~treat+batch"
div <- apply(exprs(e),2,sum)
res <- msms.edgeR(e,alt.f,null.f,div=div,fnm="treat")

str(res)
head(res)