I have a set of microarray experiments to which I have applied both the rma and gcrma preprocessing. What I have read seemed to indicate that the gcrma is better, however I am having issues with p values resulting from my relevant comparison within the study. There are 2 experimental factors, brain region and line. There are 6 chips in each group, 24 total (6 region1:line1, 6 region2.line1, etc). I applied a 2-way ANOVA resulting in p values for region, line and their interaction. When I make a frequency histogram of the results for line (the most important comparison) for RMA, the results look as expected with the largest number of pvalues close to zero. When I make the same histogram for my GCRMA results, the plot looks different, with the largest number of pvalues centered around .2. I wanted to apply statistical methods to figure out the False Discovery Rate of the tests, but I'm not sure they are relevant to these GCRMA results. Any comments on GCRMA or FDR test would be helpful. Thanks Christine

Christine. GCRMA works better than RMA. I generally recommend that a B-value cutoff be determined by spot-checking by PCR. I hope this helps, Rich On Jan 24, 2006, at 10:51 AM, cap2018 at columbia.edu wrote: > I have a set of microarray experiments to which I have applied both > the rma and gcrma preprocessing. What I have read seemed to > indicate that the gcrma is better, however I am having issues with > p values resulting from my relevant comparison within the study. > > There are 2 experimental factors, brain region and line. There are 6 > chips in each group, 24 total (6 region1:line1, 6 region2.line1, > etc). I applied a 2-way ANOVA resulting in p values for region, > line and their interaction. When I make a frequency histogram of > the results for line (the most important comparison) for RMA, the > results look as expected with the largest number of pvalues close > to zero. When I make the same histogram for my GCRMA results, the > plot looks different, with the largest number of pvalues centered > around .2. I wanted to apply statistical methods to figure out the > False Discovery Rate of the tests, but I'm not sure they are > relevant to these GCRMA results. > > Any comments on GCRMA or FDR test would be helpful. > > Thanks > Christine > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Oncoinformatics Core Lecturer Department of Biomedical Informatics Box 95, Room 130BB or P&S 1-420C Columbia University Medical Center 630 W. 168th St. New York, NY 10032 (212)305-6901 (5-6901) (voice) friedman at cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ "42 is the answer. Dylan got it wrong. 'Blowin' in the wind' is not the answer. It isn't even a number' " - Rose Friedman, age 9