Can anyone point me to a reference where these numbers regarding false positives were derived? This is a huge difference! Thanks, Jeff Sorenson > > two things to keep in mind: > > 1) rma return log2 expression so make > sure to use 2^(exp1-exp2) to compute fold change. > > 2) rma sacrifices a bit in bias for big gains in precission. for example, > using affymetrix's spike in data and using the fold-change>2 criteria > (ignoring presence absent calls) to define differentially expressed genes, > > MAS 5.0 gives you, on average, 12.5 true positives (out of 14 truely > differentially expressed genes) but 3110 false positives. > > RMA on the other hand gives you a bit smaller average # of true > positives, 11.6, but a much smaller average # of false positives: 18. > > hope this helps, > rafael > >