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

[Pita]

Thank you Anne I will look at that example, and try to work things out 
today. The one thing I dont understand is how to combine creating the 
correlations for the duplicate spots in the chips with a contrast matrix, 
or even if I need to?

Peter


At 11:11 AM 1/20/2005, Arne.Muller at sanofi-aventis.com wrote:
>Hello,
>
>Maybe the Donor effect is a random effect. You could give it a go with a 
>mixed effects model in R
>
>     lme(..., random = ~ 1|Donor)
>
>Gordon Smyth once pointed out to me and others on this list that this 
>would be similar to randomized block model that's implemented in limma 
>(section 10.3 of the limma guide).
>
>See https://stat.ethz.ch/pipermail/bioconductor/2004-December/006998.html 
>for the complete posting.
>
>         regards,
>
>         Arne
>
> > -----Original Message-----
> > From: bioconductor-bounces at stat.math.ethz.ch
> > [mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of Pita
> > Sent: 20 January 2005 16:48
> > To: bioconductor
> > Subject: [BioC] Using limma with contrast matrix ,replicate
> > spots, donor
> > effects
> >
> >
> > 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.
> >
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> >