Dear BioC useRs, I'm working on classical Golub dataset and I would like to select 10 genes that are mostly overexpressed in AML, and 10 genes that are mostly overexpressed in ALL by using Wilcoxon rank sums test. I try with the below code (I paste just the core of the loop) which compute p value for each row, but the result is identical: wilcox.AML.pvals[i] <- wilcox.test(aml.i,all.i)$p.value wilcox.ALL.pvals[i] <- wilcox.test(all.i,aml.i)$p.value Thanks in advance for any suggestion, Andrej

On 4/20/06 5:52 AM, "Andrej Kastrin" <andrej.kastrin at="" siol.net=""> wrote: > Dear BioC useRs, > > I'm working on classical Golub dataset and I would like to select 10 > genes that are mostly overexpressed in AML, and 10 genes that are mostly > overexpressed in ALL by using Wilcoxon rank sums test. > > I try with the below code (I paste just the core of the loop) which > compute p value for each row, but the result is identical: > > wilcox.AML.pvals[i] <- wilcox.test(aml.i,all.i)$p.value > wilcox.ALL.pvals[i] <- wilcox.test(all.i,aml.i)$p.value Andrej, Doing a wilcox.test just looks for differences between the two groups. With your best p-valued genes (using either list--they should be identical), you can look at the magnitude of difference in means between AML and ALL and choose genes that meet your criteria. In other words, look for the ten highest genes in your p-value list that have a mean in ALL that is higher than in AML, for example. However, this isn't really the way that most people think of differentially-expressed genes. Do you have a reason to want to look at 10 genes only? Sean

Dear Andrej, maybe I missunderstand you, since I don't see your datastructure. But I assume you want to extract a) 10 genes which are higher in AML than ALL ranked by Wilcox significance b) 10 genes which are higher in ALL than AML ranked by Wilcox significance But the wilcox.test that you are using just gives the most significantly changed gens Wilcox by default test if the two samples are different in either way Sample x<-c(1,2,3,4,5) y<-c(6,7,8,9) wilcox.test(x,y) W = 0, p-value = 0.01587 wilcox.text(y,x) W = 20, p-value = 0.01587 You can either choose to extract by Fold change or test with a different hypothesis e.g. wilcox.test(x,y,alternative="greater") #Test if x is higher than y W = 0, p-value = 1 wilcox.test(y,x,alternative="greater") #Test if y is higher than x W = 20, p-value = 0.007937 Please note that like this, the calculated p-value is halfed compared to above, but since you are only interested in ranking that shouldn't matter. HTH, bj?rn >Dear BioC useRs, > >I'm working on classical Golub dataset and I would like to select 10 >genes that are mostly overexpressed in AML, and 10 genes that are mostly >overexpressed in ALL by using Wilcoxon rank sums test. > >I try with the below code (I paste just the core of the loop) which >compute p value for each row, but the result is identical: > >wilcox.AML.pvals[i] <- wilcox.test(aml.i,all.i)$p.value >wilcox.ALL.pvals[i] <- wilcox.test(all.i,aml.i)$p.value > >Thanks in advance for any suggestion, >Andrej > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > >