[BioC] confusion using model.matrix with two-color arrays in limma

[Dick Beyer]

Hi Gordon,

Thanks very much for clarifying this.

Cheers,
Dick
*******************************************************************************
Richard P. Beyer, Ph.D.	University of Washington
Tel.:(206) 616 7378	Env. & Occ. Health Sci. , Box 354695
Fax: (206) 685 4696	4225 Roosevelt Way NE, # 100
 			Seattle, WA 98105-6099
http://depts.washington.edu/ceeh/ServiceCores/FC5/FC5.html
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On Tue, 2 Nov 2004, Gordon K Smyth wrote:

>> Date: Mon, 1 Nov 2004 13:55:12 -0800 (PST)
>> From: Dick Beyer <dbeyer at u.washington.edu>
>> Subject: [BioC] confusion using model.matrix with two-color arrays in
>> 	limma
>> To: Bioconductor <bioconductor at stat.math.ethz.ch>
>> Message-ID:
>> 	<Pine.LNX.4.43.0411011355120.12459 at hymn02.u.washington.edu>
>> Content-Type: TEXT/PLAIN; charset=ISO-8859-1; format=flowed
>>
>> I am struggling with trying to use model.matrix, which is straight-forward for
>> single color arrays, on two-color dye-swap data. For two-color data, using modelMatrix is simple
>> and straight-forward.
>>
>> I would like to know how to sort of combine these two calls, model.matrix and modelMatrix, so I
>> can easily specify my design matrix (with modelMatrix), and easily specify complex contrasts (as
>> with model.matrix).
>>
>> My particular experiment is a 2x2x2 design with factors, T,R,C each having levels 0/1.  I would
>> like to specify the interaction T*R*C, without having to explicitly type it out using
>> makeContrasts().
>>
>> How can I use model.matrix() so the dye-swaps are combined correctly (as is done easily with
>> modelMatrix), or how can I specify makeContrasts() so I don't have to type out the T*R*C
>> interaction?
>>
>> My targets are (where H signifies the case for parameter T=0, T signifies parameter T=1, etc):
>>  	Cy3	Cy5
>> 1	REF	HC
>> 2	HC	REF
>> 3	REF	HRC
>> 4	HRC	REF
>> 5	REF	TC
>> 6	TC	REF
>> 7	REF	TRC
>> 8	TRC	REF
>> 9	REF	T
>> 10	T	REF
>> 11	REF	TR
>> 12	TR	REF
>> 13	REF	H
>> 14	H	REF
>> 15	REF	HR
>> 16	HR	REF
>> My parameters are:
>>  	T	R	C
>> 1	0	0	1
>> 2	0	0	1
>> 3	0	1	1
>> 4	0	1	1
>> 5	1	0	1
>> 6	1	0	1
>> 7	1	1	1
>> 8	1	1	1
>> 9	1	0	0
>> 10	1	0	0
>> 11	1	1	0
>> 12	1	1	0
>> 13	0	0	0
>> 14	0	0	0
>> 15	0	1	0
>> 16	0	1	0
>>
>> If I use the modelMatrix and makeContrast approach, I would use something like the following to
>> get the T*R*C interaction:
>> design <- modelMatrix(targets,ref="REF")
>> fit <- lmFit(RG, design)
>> contrast.matrix <- makeContrasts("H-HC-HR+HRC-T+TC+TR-TRC",levels=design)
>> fit2 <- contrasts.fit(fit, contrasts.matrix)
>>
>> If I use the model.matrix approach (ignoring the necessary dye-swapping):
>> dd <- data.frame(
>> T=factor(c(0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0)),
>> R=factor(c(0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1)),
>> C=factor(c(1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0)))
>> design.lm <- model.matrix(~  T*R*C, data=dd, contrasts = list(T="contr.sum", R="contr.sum",
>> C="contr.sum"))
>> fit <- lmFit(RG, design.lm)
>> and have no need for the contrasts.fit() call.
>>
>> If I knew how to correctly specify a dye-flip parameter in the "dd" data.frame, I think I would be
>> where I'd like to be.
>
> Just multiply each row of design.lm by -1 whenever Cy5 is REF:
>
> dyeswap <- 1-2*(targets$Cy5=="REF")
> design.lm <- dyeswap * design.lm
> fit <- lmFit(RG, design.lm)
>
> BTW there's no compelling reason to incorporate dye-swaps into your experiment if you're using a
> common reference.  And if REF was always Cy3, then you could use model.matrix() to create the
> design matrix.
>
> Gordon
>
>> Any help or suggestions to try from anyone would be greatly appreciated.
>> Thanks very much,
>> Dick
>> *******************************************************************************
>> Richard P. Beyer, Ph.D.	University of Washington
>> Tel.:(206) 616 7378	Env. & Occ. Health Sci. , Box 354695
>> Fax: (206) 685 4696	4225 Roosevelt Way NE, # 100
>>  			Seattle, WA 98105-6099
>> http://depts.washington.edu/ceeh/ServiceCores/FC5/FC5.html
>
>
>