Hi Michael,
I am pretty sure the senior members of this group will have more to
say on
the design - but this is what I think is a solution to your problem (I
may
be wrong though :-( )
If I understand your design correctly:
It looks like a split plot design with repeated measures. Mock, Neg
and Pos
are whole plot treatments with repeated measurements at times = 24h,
48h and
72h. The samples A, B, C are crossed across all treatments?
If I am correct then I have the following question
Do you think that there is no correlation between the measurements at
the
three different times?.
If no, then I would try the something similar to section 8.2 or 11.4
(but in
this case correlation would be between times)
If yes, then this is what I would do (similar to the treatment in
limmaUsersGuide() section 11.4 -there is just an added the extra block
of
sample - A,B,C)
you can treat your design as a simple Randomized Block Design - with
A, B
and C as your blocks. The treatments are - (mock,24), (mock,48),
(mock, 72)
.. similarly 6 more treatments for the remaining neg and pos.
In situations where I am interested in contrasts like yours, I prefer
to a
cell means model matrix rather than treatment effect model matrix.
(but you
can take your call - you just need to remember what are your
coeffecients
representing)
## I may be wrong here - but this is how I think the treatments are
assigned
in the d.f.
treatments =
factor(c(rep(c(1,4,7),3),rep(c(2,5,8),3),rep(c(3,6,9),3)),labels=c("m2
4","n24","p24","m48","n48","p48","m72","n72","p72"))
## construct the design matrix for the cell means anova model
design <- model.matrix(~0+treatments)
## get the correlation with in a block ie a sample - this models the
correlations structure in a particular block
rho <- duplicateCorrelation(eset, design,
block=pData(eset)$sample)$consensus
fit1<- lmFit(eset, design, block=pData(eset)$sample, cor=rho)
contrast.matrix <- makeContrasts(m24-n24, m24-p24, levels = design) ##
constrats for the two comparsions that you mentioned
fit2 <- contrasts.fit(fit1, contrast.matrix)
fit3 <- eBayes(fit2)
Hope this helps!
Please correct me if I am wrong. This forum is a good way to re-learn
experimental design and analysis.
Thanks and Best regards,
S.
On Thu, Dec 17, 2009 at 2:38 AM, Michael Imbeault <
michael.imbeault@sympatico.ca> wrote:
> Hello,
>
> I want to analyze a recent Affymetrix Exon array dataset with the
limma
> package; I imported values from APT and created and ExpressionSet
with
> it, but the experiment's design is not the simplest and the user
guide
> is not really helping - no real explanations of the 'how' to get
there
> with the various presented use cases.
>
> It's 27 arrays, 3 time points, 3 sample sources, 3 treatments (see
data
> frame below) - I'm interested in comparing 'mock' vs 'pos' and
'mock' vs
> 'neg' at all time points (24vs24, 48vs48, etc). The samples are
paired;
> it's from the same blood donor, so i'd like to capture this effect
in
> the data to improve p-values. I did a limma analysis using
onechannelGUI
> (with great results) but it doesn't allow for complex designs (to
> capture the paired samples effect and improve p-values, increase
power),
> so I have to do this manually.
>
> So far I tried design <- model.matrix(~Treatment*Time*Sample,
> data=targets) as I seen on the mailing list, where Treatment <-
> factor(targets$Treatment), etc.
>
> Data frame:
>
> Sample ID Treatment Time Sample
> 1 Mock 24h A
> 2 Neg 24h A
> 3 Pos 24h A
> 4 Mock 24h B
> 5 Neg 24h B
> 6 Pos 24h B
> 7 Mock 24h C
> 8 Neg 24h C
> 9 Pos 24h C
> 10 Mock 48h A
> 11 Neg 48h A
> 12 Pos 48h A
> 13 Mock 48h B
> 14 Neg 48h B
> 15 Pos 48h B
> 16 Mock 48h C
> 17 Neg 48h C
> 18 Pos 48h C
> 19 Mock 72h A
> 20 Neg 72h A
> 21 Pos 72h A
> 22 Mock 72h B
> 23 Neg 72h B
> 24 Pos 72h B
> 25 Mock 72h C
> 26 Neg 72h C
> 27 Pos 72h C
>
>
> Any help would be greatly appreciated,
> Michael
>
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>
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