In short, taking into consideration a correlation coefficient allows one to estimate baseline differences. Perhaps there is a more detailed answer? Can you elaborate on creating a correlation coefficient as in 8.7 in the Limma manual? Michael On Fri, Jul 19, 2013 at 3:02 PM, P.D. Moerland <p.d.moerland@amc.uva.nl>wrote: > Dear Michael, > > This is a perfectly valid approach and has been described by Gordon in > section 3.5 'Comparisons Both Between and Within Subjects' of the edgeR > manual. Note however that baseline differences at time A between diseased > and normal cannot be estimated with this design matrix. This can be done > with the approach described in section 8.7 in the Limma manual that you > also refer to. > > best, > Perry > > --- > Perry Moerland, PhD > Room J1B-215 > Bioinformatics Laboratory, Department of Clinical Epidemiology, > Biostatistics and Bioinformatics > Academic Medical Centre, University of Amsterdam > Postbus 22660, 1100 DD Amsterdam, The Netherlands > tel: +31 20 5666945 > p.d.moerland@amc.uva.nl, http://www.bioinformaticslaboratory.nl/ > > > -----Original Message----- > From: bioconductor-bounces@r-project.org [mailto: > bioconductor-bounces@r-project.org] On Behalf Of Michael Breen > Sent: Friday, July 19, 2013 11:15 PM > To: bioconductor@r-project.org; Bioconductor Mailing List > Subject: [BioC] Limma Multi-level Experiments > > Hi all , > > My data and question can best be related to section 8.7 in the Limma > manual "Multi-level Experiments". However, lets replace Tissue with Time > with the idea to measure expression changes overtime that are different > between disease and normal. > > If I pursue this outlined route 8.7 and compare it to a very similar route > which I do not estimate the correlation between measurements made on the > same subject and use this as input to the linear model , I get very > similar with only minor differences. > > In my work-flow I create a design matrix from this information, notice > the subtle change of numbering Subjects versus section 8.7: > > FileName Subject Condition Time > File01 1 Diseased A > File02 1 Diseased B > File03 2 Diseased A > File04 2 Diseased B > File05 3 Diseased A > File06 3 Diseased B > File07 1 Normal A > File08 1 Normal B > File09 2 Normal A > File10 2 Normal B > File11 3 Normal A > File12 3 Normal B > > Condition <- factor(targets$Condition, levels=c("Control","Case")) Time <- > factor(targets$Time, levels=c("Pre","Post")) Subject <- > factor(targets$Subject) design <- > model.matrix(~Condition+Condition:Subject+Condition:Time) > > And fit the design like this: > > fit <- lmFit(exprs, design) > fit <- eBayes(fit) > > Then I form a contrast to test for genes that respond differently overtime > between disease and normal. I am still able to detect changes overtime that > are different between the two groups with extremely similar results. > > In short, am I missing anything not taking into consideration a > correlation coefficient as input to my linear model? > > Any insight is appreciated. > > Yours, > > Michael > > [[alternative HTML version deleted]] > > ________________________________ > > AMC Disclaimer : http://www.amc.nl/disclaimer > > ________________________________ > [[alternative HTML version deleted]]