[BioC] loop design question

[Ron Ophir]

Hi,
Extending the lesson of chapter 9.4. It says that "There are many valid
choices for a design matrix for such an experiment and no single
correct choice."
Would this will be a valid design:

	CD8	CD4	DN
ml12med	1	-1	0
ml13med	-1	1	0
ml14med	1	0	-1
ml15med	-1	0	1
ml16med	0	-1	1
ml17med	0	1	-1

and then 

fit <- lmFit(MA, design)
contrast.matrix
	CD8-CD4	DN-CD4	CD8-DN
CD8	1	0	1
CD4	-1	-1	0
DN	0	1	-1

fit2 <- contrasts.fit(fit, contrasts.matrix)
fit2 <- eBayes(fit2)

or because of the restriction that "With direct-design two-color data
you will need one fewer coefficient than you
have distinct RNA sources..." the design should be (created by design <
modelMatrix(targets, ref="CD4"))

                CD8 DN
ml12med   1  0
ml13med  -1  0
ml14med   1 -1
ml15med  -1  1
ml16med   0  1
ml17med   0 -1

and then the only contrast possible is 

		CD8-DN
CD8		1
DN		-1

Does the first design is over parameterized?

Thanks,
Ron


>>> "Gordon K Smyth" <smyth at wehi.edu.au> 08/06/05 5:37 PM >>>
> Date: Wed, 3 Aug 2005 08:27:13 US/Arizona
> From: scholz at Ag.arizona.edu 
> Subject: [BioC] loop design question
> To: bioconductor at stat.math.ethz.ch 
>
> Hello,
>
> I'm new to limma and have been plowing through the excellent users
guide, where
> I've reached a sticking point. Would someone have the saintly
patience to
> explain to a small mind how the design matrix for the direct design
example
> works? I think I'm missing something quite fundamental in that I was
under the
> impression that the numbers 1 and -1 represented the red and green
dyes,
> respectively, but if that is true, I have no idea what these numbers
mean in
> columns headed "CD8-CD4" and "DN-CD4". In fact, I don't really
understand what
> these "subtracted" column headers mean at all, either in the design
matrix or
> the contrast matrix. I'm planning a loop design experiment and this
appears to
> be an essential point to grasp. Thanks in advance of your answer.

The headers mean that the coefficients represent the comparisons CD8 vs
CD4 and DN vs CD4
respectively.  As the text explains, the other two treatments are
compared back to CD4.

The easiest way to analysis a direct design is to choose one of the
treatments to compare back to
in this way, i.e., to stand in as a virtual common reference.  The
design matrix for the CD
example could have been computed using

design < modelMatrix(targets, ref="CD4")

Just use the modelMatrix() function, look at the 1's and -1's, just
understand what the
interpretatation of the columns is in terms of treatment comparisons.

Gordon

> Matt Scholz
> Research Specialist
> Department of Plant Science
> University of Arizona
> scholz at ag.arizona.edu 
>
> ---------------------------------------------
> College of Agriculture and Life Sciences Web Mail.
> http://ag.arizona.edu 

_______________________________________________
Bioconductor mailing list
Bioconductor at stat.math.ethz.ch 
https://stat.ethz.ch/mailman/listinfo/bioconductor