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

[Gordon Smyth]

At 06:50 AM 25/01/2005, Pita wrote:
>I am assuming that for the design matrix, I just need to add a column for 
>each donor and plug in the  1's for the appropriate rows to my existing 
>design that specifies the infection type timepoints  (A_0,A_6, ... 
>C_42,C_72, Donor1, Donor2, etc ...) ???
>
>Is this correct?

That sounds correct.
Gordon

>Peter
>
>At 12:17 AM 1/22/2005, Gordon Smyth wrote:
>>Having within-array replicate spots on your arrays makes no difference at 
>>all to the design and contrast matrices. (With one exception, which is 
>>that you can't fit a random block effect in limma and estimate a 
>>duplicate spot correlation at the same time.) Is there something which 
>>has caused you to become concerned about this?
>>
>>I suggest you try accommodating the donor effect simply by including a 
>>set of coefs for the donor effects in your design matrix. You form the 
>>design matrix as you would for an additive two-way anova with donor as 
>>one of the two factors. Comparisons between infections, infect types, and 
>>infect times will then be in effect made _within_ donor.
>>
>>Gordon
>>
>>>Date: Thu, 20 Jan 2005 10:48:21 -0500
>>>From: Pita <pwilkinson_m at xbioinformatics.org>
>>>Subject: [BioC] Using limma with contrast matrix ,replicate spots,
>>>         donor effects
>>>To: bioconductor <bioconductor at stat.math.ethz.ch>
>>>
>>>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.