Dear BioStar Users,
I am new in the BioStar community, I tried to search for posts that answer my question but I couldn't find any. Please forgive me if this has been already answered.
I need to analyze a microarray dataset so I decided to use the bioconductor package I knew for this task, limma. Limma uses linear models to find Differentially Expressed Genes (DEGs) between 2 groups that provides p-values and q-values.
However, a colleague of mine suggested me to use SAM: Significance Analysis of Microarrays available for R in CRAN as samr. SAM uses a non-parametric approach to find DEGs based on a d score (reflecting the difference of the means of the 2 groups) and a FDR.
My question is, how do you decide between them? which one do you think is "better"?
A related question is, if you decide to use SAM, how do you prepare exploratory graphs such as volcano plots?
Thanks for your hep!