[BioC] limma: paired + multiple comparisons + technical replication?

[Sarah Bonnin]

Dear Gordon,

Thank you for your answer.

The only thing is that I also want to compare all groups (1 to 1, all possibilities), and use the "makeContrasts" command - so as to automate better - and I'm not sure if my way of doing it is correct!

Here is my code, which actually works now, but I was wondering if it was really taking into account pairs as it should.

targets (a bit more informative than the previous one; TR is technical replicate)
FileName	Groups		Pairs
Sample1	Control		1
Sample1_TR	Control		1
Sample2	Control		2
Sample3	Control		3
Sample4	Control		4
Sample5	Treat1		1
Sample5_TR	Treat1		1
Sample6	Treat1		2
Sample7	Treat1		3
Sample8	Treat1		4
Sample9	Treat2		1
Sample9_TR	Treat2		1
Sample10	Treat2		2
Sample11	Treat2		3
Sample12	Treat2		4

techrep <- c(1,1,2,3,4,5,5,6,7,8,9,9,10,11,12) # technical replication info

design <- model.matrix(~0+targets$Groups+targets$Pairs)
  colnames(design) <- c(unique(targets$Groups),"Pairs")

corfit <- duplicateCorrelation(xx, design, ndups = 1, block = techrep)
fit <- lmFit(xx, design, block = techrep, cor = corfit$consensus)
  
contrast.matrix <- makeContrasts(contrasts=c("Treat1-Control", "Treat2-Control", "Treat2-Treat1"), levels=design)
  fit <- contrasts.fit(fit, contrast.matrix)
  fit <- eBayes(fit)


Thank you!

Sarah




-----Original Message-----
From: Gordon K Smyth [mailto:smyth at wehi.EDU.AU] 
Sent: Thursday, October 03, 2013 1:04 AM
To: Sarah Bonnin
Cc: Bioconductor mailing list
Subject: limma: paired + multiple comparisons + technical replication?

Dear Sarah,

Just put Pairs in the design matrix, for example

   model.matrix(~Groups+Pairs)

and use duplicateCorrelation() for the biolreps.

Best wishes
Gordon

> Date: Tue, 1 Oct 2013 17:55:34 +0200
> From: Sarah Bonnin <Sarah.Bonnin at crg.eu>
> To: "bioconductor at r-project.org" <bioconductor at r-project.org>
> Subject: [BioC] limma: paired + multiple comparisons + technical
> 	replication	?
>
> Dear list,
>
> This question might be a bit redundant and I apologize for it, if it 
> is, but I can't find a clear answer to what I'm trying to do.
>
> I am working on a set of 12 expression one-channel arrays.
>
> My target file is basically as follows:
> FileName Pairs Groups
> Sample1        1 Group1
> Sample2        1 Group1
> Sample3        1 Group2
> Sample4        1 Group2
> Sample5        1 Group3
> Sample6        1 Group3
> Sample7        2 Group1
> Sample8        2 Group2
> Sample9        2 Group3
> Sample10        3 Group1
> Sample11        3 Group2
> Sample12        3 Group3
>
> There are several parameters to take into account:
>
> - I want to produce all possible pairwise comparisons (Group3-Group2, 
> Group2-Group1, Group3-Group1): "Groups" column
>
> - I want my design to take into account the paired samples: "Pairs" 
> column
>
> - The last thing is that some samples are technical replicates 
> (Sample1 with Sample2, Sample3 with Sample4, Sample5 with Sample6) and 
> I would also like to take this into account.
>
> I've read the "8.7 Multi-level experiments" chapter of limma user guide, which guided me into combining paired data and multiple comparisons, in which case I would do:
>> design <- model.matrix(~0+factor(targets$Groups))
>> colnames(design) <- unique(targets$Groups) corfit <- 
>> duplicateCorrelation(eset,design,block=targets$Pairs)
>> fit <- 
>> lmFit(eset,design,block=targets$Pairs,correlation=corfit$consensus)
>
> In theory to take into account technical replicates I would use:
>> biolrep <- c(1,1,2,2,3,3,4,5,6,7,8,9) corfit <- 
>> duplicateCorrelation(eset, block = biolrep) fit <- lmFit(eset, block 
>> = biolrep, cor = corfit$consensus)
>
> But how can I combine all of this?
>
> Is there a way to somehow pass both paired and technical replication 
> information into the duplicateCorrelation step? Or should I modify the 
> design instead to take into account the paired design?
>
> It is getting quite confusing for me.
>
> Any help greatly appreciated!
>
> Thanks in advance!
>

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