[BioC] limma and blocks
Thibaud-Nissen, Francoise
fthibaud at tigr.org
Mon Nov 8 18:07:22 CET 2004
Fangxin,
Thank you very much for your answer. I derived my models from the technical replicates examples in the Limma manual. I am glad you can confirm that model 1 corresponds to a random effect model and model 2 to a fixed effect model. You seem to hint that with two levels for block a fixed-effects model would be more appropriate. Is this correct? For a given factor, is a fixed-effects model better if you have fewer levels?
Thanks,
Françoise
-----Original Message-----
From: Fangxin Hong [mailto:fhong at salk.edu]
Sent: Friday, November 05, 2004 6:17 PM
To: Thibaud-Nissen, Francoise
Cc: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] limma and blocks
I think you can read the new limma User's Guide, section 9.4. should
answer your questions. I am sorry that I did notice that you have only two
blocks (growth condition), tht way maybe model 2 is better.
Limma User's Guide, available from
http://bioinf.wehi.edu.au/limma/usersguide.pdf
Fx
> Hi,
>
>
>
> I am analyzing an experiment using 32 Arabidopsis Affymetrix chips. It
is
> basically a 2x2x4 design repeated twice using different biological
replicates (the third replicate will be provided later). The plants
within
> each biological replicate were grown at the same time, and in that sense
are related and form a block. There are no technical replicates within
each block.
>
>
>
> I am using limma. In the model I calculate separate coefficients for
each
> of the 16 conditions. I then use contrasts matrices to evaluate
contrasts
> of interest.
>
>
>
> I now would like to incorporate the block effect in my model in order to
account for random variation in the growth conditions between the two
biological replicates.
>
>
>
> I tried two models that give different results, but I am not sure any of
them is correct:
>
>
>
> If the first biological replicate appears first in my design, and
"design"
> is my design matrix for the 16 coefficients:
>
>
>
> Model 1:
>
> biorep <- c(rep(1,16),rep(2,16))
>
> fit <- lmFit(mydata, design, block= biorep)
>
> fit <- eBayes(fit)
>
> ...
> Model 2:
>
> blockdiff <- c(rep(1,16),rep(-1,16))
>
> blockdesign <- cbind(design, Block=blockdiff)
>
> fitblock <-lmFit(mydata, blockdesign)
>
> fitblock <- eBayes(fitblock)
>
> ...
>
> I would appreciate any tip that could put me in the right track!
>
>
>
> Thanks,
>
>
>
> Françoise
>
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