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
>
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