Hi All, I have extended the GSA code (http://www-stat.stanford.edu/~tibs/GSA/) to include lmFit() from the limma package so as to have linear model capabilities with GSA. Basically, I'm using the modified t-statistic values from lmFit just like the SAM-like t-statistic values are used in the GSA code. I was wondering if anyone had any thoughts on whether this was, in principle, an OK thing to be doing. I am worrying about whether there is an underlying issue I'm not aware of in using the moderated t-statistic values from limma as opposed to the SAM t-statistic values that uses the s0 term in the denominator. My tests on some microarray data I have shows that in a qqplot of t-statistic values from the two methods, they are in pretty close agreement except for large values of the t-values. If anyone knows of reasons not to be doing this or could point me to places with possible explanations, I'd be very grateful. Cheers, Dick ********************************************************************** ********* Richard P. Beyer, Ph.D. University of Washington Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695 Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100 Seattle, WA 98105-6099 http://depts.washington.edu/ceeh/ServiceCores/FC5/FC5.html http://staff.washington.edu/~dbeyer