Dear Victor, Thanks for contacting me about PLGEM. There is a mailing list to which to address questions about Bioconductor packages. I suggest you to sign up for it (if you haven't done so yet) and address your question there. I will be happy to answer you through that forum. This is to keep track of threads and to benefit the community. I am CCing the mailing list to keep track. To give you a quick answer, PLGEM is not restricted to use with microarray or proteomics data. Although these two are the datasets that have been validated and tested so far, I suspect there will be other types of data that might be described by a power law relationship between the standard deviation and the mean. Metabolomics might be one of them, but I never tried to fit a PLGEM on a metabolomic dataset. If you like, you can send me an example file and I can try to see if it fits. Regarding normalization, this is very much data-type-specific. I have no experience analyzing metabolomic data, so I have no specific recommendations to make. I suggest you to collaborate with a statistician to try to find the best normalization method for your specific data. again, if you like, you are welcome to send me an example file, and I can try to see if simple normalization methods might be "good enough", but I will take no responsibility for your final choice. :-) Anyone out there with experience with normalization of metabolomic data that can chip in and give Victor some advice? Hope this helps for now! Good luck with your analysis. Best, Norman From: Victor Nesati [mailto:lsivn@nus.edu.sg] Sent: Monday, 1 October, 2012 3:55 PM To: Norman Pavelka (SIgN) Subject: PLGEM Hello Norman, Quite some time ago I read your paper on PLGEM and actually used a bit Of it in my former spectral count based quantitative proteomics project back in Suizzera. Now I found myself quite close to you dealing with a bit different MS related project. Though it seems to me that even here your PLGEM may be quite applicable. We are doing discovery based metabolomics and after a number of Orbitrap runs comparing different samples I found myself in quite familiar Mass vs intensity matrix territory. Having playing with it quite a while I found that my results on the ID-ing statistically significant features are greatly varying depending on the choice of scaling, pre stat test normalization procedure and stat test itself. So I was wondering a bit about possibility of using your PLGEM in this settings. Brief literature search showed that Microarray normalization Procedures working for mRNA containing more than 10,000 features are not working that well for miRNA containing much less. We have something around 6000-9000 masses in the matrix. What would your suggestion and choice of normalization, stat test to identify most likely candidates. With best wishes Victor