Limma Multi-level Experiments
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Michael Breen ▴ 370
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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]]
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Michael Breen ▴ 370
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Last seen 9.6 years ago
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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 at 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 at 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
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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]]
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