Dear all, I was wondering why there is so many difference between the two following approaches to handle the replication for my experiments - therefore, I don't which one to trust. briefly, Here is my target : Cy3 Cy5 wt1 mu1 mu1 wt1 wt2 mu2 mu2 wt2 wt3 mu3 mu3 wt3 to get the gene differentially expressed between Mutant and WT, I have stricly followed the two solutions given in the page 37 of limma userguide (3rd apriol 2010): - the first one (page 37) is using duplicateCorrelation - the second one clearly explicit the design matrix and the contrast matrix (page 38) as follow design = cbind( R1_MuvsWT = c(-1,1,0,0,0,0), R2_MuvsWT = c(0,0,-1,1,0,0), R3_MuvsWT = c(0,0,0,0,-1,1) ) fit = lmFit(MAn,design) cont.matrix = makeContrasts ( MuvsWT = (R1_MuvsWT + R2_MUvsWT+R3_MUvsWT)/3, levels = design ) using these two approaches give quantitatively different results. Which one should I trust ? Thanks by advance for any pieces of advice and / or any help Cheers -- G.M -- Guillaume Meurice - PhD Bioinformaticien Unité de Génomique Fonctionnelle PR2 - Bureau 323.2 Poste : 3509 Institut Gustave Roussy - PR2 114 rue Edouard Vaillant - 94805 VILLEJUIF Cedex tel : +33 (0)1 42 11 42 11 (poste 3509) fax : +33 (0)1 42 11 62 67 [[alternative HTML version deleted]]

Hi Guillaume, Please don't resend identical questions to the list with different subject lines. For that matter, please don't resend identical questions with the same subject line either. I understand that this question is interesting to you, but you have to understand that the purpose of this list is to answer technical questions about how to use BioC packages. Your question is statistical in nature, and not many are interested in teaching statistics via email. This is especially true given the amount of information available on the subject on the internet. If you read the limma User's Guide closely, you will see that you are fitting two different models. One is a standard ANOVA model, and the other is a mixed model. Simplistically, the difference between the two is that ANOVA assumes that each group you are modeling has similar intra-group variance, and the only difference is the mean expression. A mixed model is a more general model that accounts for any differences in intra-group variances. So you should not be particularly surprised that there are different results, since you are fitting different models. The question isn't which one you should trust. The real question is what assumptions are you willing to make about your data and why. Best, Jim On 2/18/2011 8:15 AM, Guillaume Meurice wrote: > Dear all, > > > I was wondering why there is so many difference between the two following approaches to handle the replication for my experiments - therefore, I don't which one to trust. > > > briefly, Here is my target : > Cy3 Cy5 > wt1 mu1 > mu1 wt1 > wt2 mu2 > mu2 wt2 > wt3 mu3 > mu3 wt3 > > > to get the gene differentially expressed between Mutant and WT, I have stricly followed the two solutions given in the page 37 of limma userguide (3rd apriol 2010): > - the first one (page 37) is using duplicateCorrelation > - the second one clearly explicit the design matrix and the contrast matrix (page 38) as follow > > design = cbind( > R1_MuvsWT = c(-1,1,0,0,0,0), > R2_MuvsWT = c(0,0,-1,1,0,0), > R3_MuvsWT = c(0,0,0,0,-1,1) > ) > fit = lmFit(MAn,design) > > cont.matrix = makeContrasts ( > MuvsWT = (R1_MuvsWT + R2_MUvsWT+R3_MUvsWT)/3, > levels = design > ) > > > using these two approaches give quantitatively different results. > > > Which one should I trust ? > > Thanks by advance for any pieces of advice and / or any help > > Cheers > > -- > G.M > -- > Guillaume Meurice - PhD > Bioinformaticien > > Unit? de G?nomique Fonctionnelle > > PR2 - Bureau 323.2 > Poste : 3509 > > Institut Gustave Roussy - PR2 > 114 rue Edouard Vaillant - 94805 VILLEJUIF Cedex > tel : +33 (0)1 42 11 42 11 (poste 3509) > fax : +33 (0)1 42 11 62 67 > > > [[alternative HTML version deleted]] > > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > 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 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues