Entering edit mode
In an experiment with 3 or more conditions (treatments, levels ...)
it is usually necessary to have more contrasts than can be
accommodated by the design matrix.
That is why there is fit.contrast as well as lmFit.
My approach is to loop designs is to use single channel analysis. It
is simplest to pick an arbitrary condition as the reference for the
design matrix, or to leave out the intercept and fit all the
conditions. (This puts the condition mean in the "M"
matrix.) Either way, the main purpose of this step is to get the MSE
for each gene, which is required for the contrasts.
Then I do all the contrasts of interest as calls to fit.contrast.
My paper on using single channel analysis for loop designs can be
found
at http://www.stat.psu.edu/~zhao/bcc/respapers/AltmanInterface04.doc
but currently it does not discuss the limma implementation of this.
--Naomi
At 06:21 AM 12/9/2005, michael watson (IAH-C) wrote:
>Hi
>
>I would like to respectfully request more examples of the creation of
>design matrices for complex (ie involving more than 3 samples) loop
>designs using two-colour microarrays in the limma documentation.
>
>Although section 20.5 does give an example, with 5 RNA samples, one
of
>these is a pool which makes an obvious reference when creating the
>design matrix as it is a coefficent we are not interested in
estimating.
>However, other loop designs may not contain such an obvious sample,
and
>I am struggling to figure out a way to create a design matrix for my
>current design that allows all contrasts of interest. In fact, at
>present, I have to create one design matrix to get some of the
contrasts
>and another to get the rest. Is this a valid approach?
>
>Many thanks
>
>Mick
>
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Naomi S. Altman 814-865-3791 (voice)
Associate Professor
Dept. of Statistics 814-863-7114 (fax)
Penn State University 814-865-1348
(Statistics)
University Park, PA 16802-2111
