I am trying to use Limma to analyze differential expression in processed, log2 transformed proteomics data. I have been following this code: http://www.biostat.jhsph.edu/~kkammers/software/CVproteomics/R_guide.html
However, for some of the analysis the p-values look very strange and the volcano plots show no spread, they are all in a line.
Looking deeper into the analysis outputs it seems as it is apparent something is going wrong with eBayes step. s2.post (posterior value of variance) is the exact same as s2.0 (estimated prior value of variance) and does not differ between gene. and the df.0 (degrees of freedom for s2.0 ) is inf
Any insight into what might be going wrong or how I can trouble shoot would be appreciated!
0 answers
No answers yet.
Log in to answer this question.
You might post this to the bioconductor site support.bioconductor.org), where Gordon Smyth (the limma author) will more quickly see it.
Agree with Devon. But either here or in the bioconductor site you should include the actual code you used to obtain your results, or you may get little help. It might be a dataset issue, so if you used publicly available data it would be helpful to describe it: e.g. give GEO id and code used to preprocess it. Ideally, and if possible, a minimum reproducible example will give you the highest chances of getting help. In the bioconductor site it is also requested to include the output of
sessionInfo()(to check the version of R and packages).