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= Using R to evaluate DGE for differential expression =

'''Moderated statistical tests for assessing differences in tag abundance.''' (2007). Robinson MD, Smyth GK. ''Bioinformatics''. 23(21):2881-7. [http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&uid=17881408 PMID: 17881408]

The authors of this paper develop a statistical test using a negative binomial distribution to model noise in the data. They release R code (available at [http://bioinf.wehi.edu.au/resources/ bioinf.wehi.edu.au/resources/]) to support their treatment, but it is uncommented, and not functional as is.

Here I apply the code and examine the results in a sample data set.

At the website above you can find a tar archive called: '''msage_0.7.tar.gz'''. I can't get the package to install in R, so I simply unpack it and use the .R files directly. In addition, the code calls a code block (kepler.R) which is not included in the distribution. However, it is available here: http://dulci.biostat.duke.edu/sage/sage_analysis_code.r 

Basically there are two files of R code: fitNBP.R and msage.R that can be sourced. The file msage.R has a source statement inside: source("kepler.R"), however this code block is not included. To get the code to function, download ''sage_analysis.r'', and rename it ''kepler.R'' (written by Thomas Kepler at Duke). It contains a function called: glm.poisson.disp() that is called by msage.R.

{{{
# set working directory and load library functions
setwd("E:/cry1/Solexa/Smyth")
source("fitNBP.R")
source("msage.R")
}}}


CategoryR