yes, you are right, i applied a wilcoxon test to the M values (which is in this case the same as the paired wilcox of the log2 expression values). i got a vector of p values, one p value for each gene. the p values i got were a little bit surprising to me, because i found genes significant, although they were not that much different between the sample and the control group. something about 6000 genes have a p value less then 0.05, so this might be ok (i was a little bit too quick by saying that every gene is significantly different :) ). so the next step is to correct the p values... i thought correcting p values is only necessary when i do multiple testing? sorry for my question, but i am more used to do some programming and work with databases then doing statistics... thanks to all your answers, you help me very much! thanks! Quoting Naomi Altman <naomi@stat.psu.edu>: > If I understand what you did, you should have only 1 column of > p-values - 1 per gene. So, I think your apply command did not work > as you expected (although I think it should have). > > My understanding is that you have 2 arrays per patient and took the > 13 M values. Applying a Wilcoxon test to each row should test that > the median difference is 0. > > Try doing the test on a couple of rows and then compare with the > output you obtained. > > After you get 1 p-value per gene, you should apply a multiple > comparisons adjustment. FDR is popular and can be computed using the > "qvalue" library in Bioconductor. > > --Naomi > > At 09:44 AM 2/11/2005, Dipl.-Ing. Johannes Rainer wrote: >> hi, >> i must excuse myself for my question, but i'm not really good in >> statistics... >> >> we have done affymetrix genechips with samples from patients before >> and after treatment. until now i searched for genes that are >> influenced by the treatment using M values but i wanted also to >> apply a statistical test to get some proof that the genes i found >> are significant. >> >> so i applied a wilcoxon paired test to the expression values (one >> test per gene). my samples size is 13 (13 chips with samples before >> treatment and 13 afterwards). i subtracted the values after >> treatment from those before treatment ( >> >> p.vals <- apply((untreated-treated),MARGIN=1,wilcox.test) , >> untreated is a matrix with 13 columns and 54000 rows (genes) and the >> same is treated). according to the p values i got nearly every gene >> is significant, also if the gene is not regulated. >> >> so my question, do i have to correct the p values or was i totally >> wrong with the assumption to get significant (and regulated) genes >> in this way? >> >> thanks >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Bioinformatics Consulting Center > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > >