Hi Julie, Your calculation of df is off. You lose one df for each column of your design matrix, so you have 2 residual df. The computation of the prior df is outlined (I believe) in the first paper cited in the limma User's Guide: http://www.bepress.com/sagmb/vol3/iss1/art3 Best, Jim Julie Zhu wrote: > Hi James, > > Thanks you so much for helping me solving the mystery! Could you please > explain why the degree freedom is so large? I only have 4 arrays, so total 3 > df (n-1). One is used for estimating dye effect, 1 is used for estimating > mutant effect, so the residual is 1 df. Why is there an additional of > 2.397083 df added? Is it because the information borrowing from the > neighboring genes? How is the df.prior calculated? Thanks so much for > sharing your knowledge! > > Best regards, > > Julie > > > On 9/17/09 4:50 PM, "James W. MacDonald" <jmacdon at="" med.umich.edu=""> wrote: > >> >> Julie Zhu wrote: >>> Dear Gordon, >>> >>> I noticed that there is inconsistency between the t and p.value on the >>> topTable output using Limma package to analyze a dye swap two channel array >>> experiment. >>> logFC AveExpr t P.Value adj.P.Val B >>> 1401 3.216254 11.74137 22.49546 1.041470e-05 0.07874703 3.435097 >>> 1537 3.344735 11.88007 18.07448 2.702061e-05 0.07874703 2.951114 >>> >>> The two-tailed p-value for t=22.49546 with df=1 would be 0.028 >>> 2*pt(-abs(22.49546),df=1), but the P.Value in the above output is 1.04e-05. >>> Did I miss something? Could you please explain the inconsistency? Thanks! >> The degrees of freedom will be somewhere around 3, but a bit larger >> because of the ebayes fit. You can get the actual value from your >> MArrayLM object, so what you really want is >> >> 2*pt(-abs(22.49546), fit$df.prior + fit$df.residual[1]) >> >> assuming you did something like >> >> fit <- lmFit(<stuff>) >> fit <- eBayes(fit) >> >> Best, >> >> Jim >> >> >>> Here is the design matrix and relevant code snippets. >>> >>> Dye mutant >>> 1 -1 >>> 1 -1 >>> 1 1 >>> 1 1 >>> >>> design = cbind(Dye=1, mutant = c(-1,-1,1,1)) >>> fit = lmFit(MA, design) >>> ### MA is a MAlist object >>> fit3 = eBayes(fit) >>> topTable(fit3, number=2, coef="mutant", adjust="fdr") >>> >>> Here is my R session information. >>> >>> R version 2.9.0 (2009-04-17) >>> i386-apple-darwin8.11.1 >>> >>> locale: >>> en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8 >>> >>> attached base packages: >>> [1] stats graphics grDevices utils datasets methods base >>> >>> other attached packages: >>> [1] limma_2.18.0 >>> >>> Thank you very much for your help! >>> >>> Best regards, >>> >>> Julie >>> >>> _______________________________________________ >>> 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 > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826