Hi I have a dataset which is pretty much IDENTICAL to the swirl dataset: Experiment 1 - two replicate arrays with a dye swap: TreatedCy5 vs UntreatedCy3 UntreatedCy5 vs TreatedCy3 Experiment 2 - two replicate arrays with a dye swap: TreatedCy5 vs UntreatedCy3 UntreatedCy5 vs TreatedCy3 This is fantastic because I can basically just copy and paste the instructions from the limma userguide.pdf document to get my differentially expressed genes. However, I want to do a comparison of limma with a "normal" method of analysis - say a t-test. How would I carry out a t-test on this kind of data? For example, the top gene limma pulls out has these values for my four arrays for log2(Cy5/Cy3): -2.7, 2.7, -2.7, 3 This makes sense as the experiment contains a dye-swap, so if I flip my log(ratios) such that I am always comparing treated/untreated, my values are -2.7, -2.7, -2.7 and -3. BUT how would I go about doing a t-test on this kind of comparison??? (I know there are huge arguments against doing such a thing, but humour me). I mean, I basically have four values for the same thing (the relative expression of treated against untreated) and if I was doing a t-test - what am I comparing the values against? Thanks in advance Mick

In this case you want to do a one-sample t-test where you are testing to see if the true mean is different from zero. You could simply use the t.test function in R: t.test(c(2.7,2.7,2.7,3)) One Sample t-test data: c(2.7, 2.7, 2.7, 3) t = 37, df = 3, p-value = 4.342e-05 alternative hypothesis: true mean is not equal to 0 95 percent confidence interval: 2.536317 3.013683 sample estimates: mean of x 2.775 You can do that for all the data in an exprSet if you use the esApply functionality in Biobase. There is a vignette that explains how to do that for a two-sample t-test, which should give a pretty clear roadmap for the one-sample case. HTH, Jim James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 >>> "michael watson (IAH-C)" <michael.watson@bbsrc.ac.uk> 06/02/04 09:26AM >>> Hi I have a dataset which is pretty much IDENTICAL to the swirl dataset: Experiment 1 - two replicate arrays with a dye swap: TreatedCy5 vs UntreatedCy3 UntreatedCy5 vs TreatedCy3 Experiment 2 - two replicate arrays with a dye swap: TreatedCy5 vs UntreatedCy3 UntreatedCy5 vs TreatedCy3 This is fantastic because I can basically just copy and paste the instructions from the limma userguide.pdf document to get my differentially expressed genes. However, I want to do a comparison of limma with a "normal" method of analysis - say a t-test. How would I carry out a t-test on this kind of data? For example, the top gene limma pulls out has these values for my four arrays for log2(Cy5/Cy3): -2.7, 2.7, -2.7, 3 This makes sense as the experiment contains a dye-swap, so if I flip my log(ratios) such that I am always comparing treated/untreated, my values are -2.7, -2.7, -2.7 and -3. BUT how would I go about doing a t-test on this kind of comparison??? (I know there are huge arguments against doing such a thing, but humour me). I mean, I basically have four values for the same thing (the relative expression of treated against untreated) and if I was doing a t-test - what am I comparing the values against? Thanks in advance Mick _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor