msms.edgeR {msmsTests} | R Documentation |
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
msms.edgeR(msnset,form1,form0,facs=NULL,div=NULL,fnm=NULL)
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. |
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
.
A data frame with column names 'LogFC', 'LR', 'p.value', with the estimated log fold changes, likelihood ratio statistic and corresponding p-value as obtaimed from a call to glmLRT() from the edgeR package.
Josep Gregori i Font
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
MSnSet
, edgeR
, glmLRT
, msmsEDA
## 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)