On 10 Jun 2010, at 17:54, Ina Hoeschele wrote: > Hi, I have been analyzing an affy microarray dataset with 2 treatments and 1 control, which do not have replication (so total of 3 chips), with several methods that do not require replication, including bgx. BGX seems to give the poorest results which I did not expect, so I am wondering whether I am doing something wrong. I ran BGX as follows: >> library(bgx) >> Data <- ReadAffy() >> bgx(Data,burnin=9216,iter=20480,output="minimal") >> bgxOutput <- readOutput.bgx("run.1") >> rankedGeneList1 <- rankByDE(bgxOutput,conditions=c(1,2),absolute=TRUE) #comparing control to treatment 1 >> rankedGeneList2 <- rankByDE(bgxOutput,conditions=c(1,3),absolute=TRUE) #comparing control to treatment 2 >> plotDEHistogram(bgxOutput, conditions=c(1,2),normalize="none") #I am doing this to get the number of differentially expressed probesets > Number of differentially expressed genes (left): 1 > Number of differentially expressed genes (right): 590 > Total number of differentially expressed genes: 591 >> plotDEHistogram(bgxOutput, conditions=c(1,3),normalize="none") > Number of differentially expressed genes (left): 31 > Number of differentially expressed genes (right): 106 > Total number of differentially expressed genes: 137 > > This then gives me the lists of differentially expressed genes: >> list1 <- rankedGeneList1[1:591,] >> list2 <- rankedGeneList2[1:137,] > Could you try using normalize="loess" ? The histogram should be centred on 0.5 and have peaks near 0 and 1. The spline fit should be smooth. What do your plotDEHistogram() plots look like? Thanks E > Thanks for any comments ... > Ina