[BioC] Using limma with contrast matrix ,replicate spots, donor effects

[Pita]

This question is because I am misunderstanding how certain things fit 
together in Limma. There is no example like this in the documentation, and 
I am trying to figure out how to do this based on examples section 10.5 
and  14.1.

sorry for the lengthy post, this is a complicated one, but it might be an 
interesting case example for some of you.

A simplified version of my experiment follows

Background:

Blood from 8 separate donors have been collected and undergone a cell sort. 
The sorted cells that we are interested in were divided and infected with 
HIV according to the following table (the letters do not mean the literal 
HIV subtype in this case, I have just simplified it to A,B,C and 
N=non-infected.).

Filename 	Cy3	Cy5	Donor	
1		Ref	N_0	1
2		Ref	N_6	1
3		Ref	N_24	1
4		Ref	N_74	1
5		Ref	A_0	1
6		Ref	A_6	1
7		Ref	A_24	1
8		Ref	A_74	1
9		Ref	B_0	1
10		Ref	B_6	1
11		Ref	B_24	1
12		Ref	B_74	1
13		Ref	C_0	1
14		Ref	C_6	1
15		Ref	C_24	1
16		Ref	C_72	1
...for 7 more donors

-  I have a series of 2 channel array hybridizations against a common reference
- the array used uses DUPLICATE spots (spacially spotted in pairs).
-  N is non-infected(this exp its HIV),
-  A,B,C are three different infection types
-  0,6,24 are the times that the cells were harvested and RNA isolated.
-  A_0 is infected at time 0 which is different from non-infected 0 (N_0) 
in that A_0 is after 2 hours of incubation with the virus.
- Total of 8 donors

The question I have is how to deal with the ' donor effect' using Limma. 
First case (1): I could assume that my donor variability is much less than 
the variability in the treatments and just plow ahead(probably worth 
trying).  In the second case (2), the problem being that there can be quite 
the donor variability so I am thinking that what might be better is if I 
subtract the 0 time point for each infection type WITHIN each donor from 
all the others so that all expression values are relative to 0:

For 
example   Donor1  N_72-N_0,  N_24-N_0,  N_6-N_0,        A_72-A_0, 
A_24-A_0,  A_6-A_0,    etc
		Donor1 
N_72-N_0,  N_24-N_0,  N_6-N_0,        A_72-A_0,  A_24-A_0,  A_6-A_0,    etc


I would like to compare the difference between each donor for the 
non-infected N to characterize the donor variability so that I understand 
it and I would like to compare the infection types for each time point in 
the 2 different ways (cases). My ultimate goal it to compare the infection 
types at each time point against each other while reducing the noise due to 
donor variability.

There are 2 things i need to know how to do

How do I combine creating the contrast matrix and use it with calculating 
duplicate spot correlation in 14.1,  for case 1?
How do I create a contrast matrix to account for normalising against time 0 
as in case (2) and then combine that with the duplicate spot correlation?


lastly, are there in fact other proven methods for dealing with donor 
variability ?

Thanks for any insight.

Peter W.