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

[Gordon Smyth]

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.