Dear list members and especially limma users,
For the absence of examples about factorial design using two-color
arrays* in limma user's guide,I resort to this email list in the hope
of
getting a hint.
(* Chapter 8.7 takes single-color arrays for example.
* Chapter 11.5 takes two-color arrays for example. However, when
constructing design matrix, a common reference is chosen, which makes
it
the same way to make contrast matrix as in single-color arrays.)
Previously Adrian Johnson posed a question about limma factorial
design
and proposed two answers for discussion. Later Naomi Altman gave
another
solution. I have no idea which answer is right and I'd like to post my
answer here for discussion.
WT and MU are untreated while wt and mu are treated (see diagram
below).
The linking lines stand for hybridizations and there are dye-swap
hybridizations in the four comparisons. The interested contrasts are
mu-MU, wt-WT, and (mu-MU)-(wt-WT). I make the design matrix using WT
as
ref. My way of making contrast matrix may be controversial but I think
it is reasonable. The contrast matrix wt-WT is set to wt
("wt_WT"=wt),
because WT is used as ref in making design matrix thus wt means
comparison with WT implicitly. There should not be an argument about
"mu_MU"=mu-MU. As to the contrast matrix "(mu_MU)_(wt_WT)"=(mu-MU)-wt,
it can be explained in the same way as "wt_WT"=wt.
Although I consider it reasonable, I am not that confident. Any
feedback
is welcome.
The diagram for the experimental design and the code are listed below.
For the convenience of comparison and discussion, Adrian and Naomi's
methods are also pasted below.
wt----mu <--treated
| |
WT---MU <--untreated
> targets<-read.table("targets.csv",header=T)
> targets
Cy3 Cy5 FileName
1 wt WT 1.gpr
2 mu MU 2.gpr
3 MU WT 3.gpr
4 mu wt 4.gpr
5 WT wt 5.gpr
6 MU mu 6.gpr
7 WT MU 7.gpr
8 wt mu 8.gpr
> design<-modelMatrix(targets,ref="WT") #<----WT as ref!
Found unique target names:
mu MU wt WT
> design
mu MU wt
[1,] 0 0 -1
[2,] -1 1 0
[3,] 0 -1 0
[4,] -1 0 1
[5,] 0 0 1
[6,] 1 -1 0
[7,] 0 1 0
[8,] 1 0 -1
>
contrast.matrix<-makeContrasts("wt_WT"=wt,"mu_MU"=mu-
MU,"(mu_MU)_(wt_WT)"=(mu-MU)-wt,levels=design)
> contrast.matrix
Contrasts
Levels wt_WT mu_MU (mu_MU)_(wt_WT)
mu 0 1 1
MU 0 -1 -1
wt 1 0 -1
Adrian Johnson wrote:
> Dear Prof. Smyth and experts,
>
> I am writing to you as a final option, after not finding an
> appropriate answer in BioC mailing list, internet and online
> tutorials.
> Although the documentation is the 'best reference', one particular
> piece is missing in the documentation may be due to its complexity.
> That is factorial design of a two-color microarray data.
>
> We are using mouse agilent chips for identifying effect of a drug on
a
> mutant vs. normal mouse strain. We used dye-swap for some chips.
The
> target files looks like this:
>
>> targets
>>
> Array Cy3 Cy5 Treatment Shock
> 1 Array1 WT MU Y H
> 2 Array2 WT MU N C
> 3 Array3 WT MU Y H
> 4 Array4 MU WT N C
> 5 Array5 WT MU Y C
> 6 Array6 WT MU Y C
>
> The aim of the experiment is to find differentially expressed genes
> between MU and WT and importantly find genes differing between
> MU.Treated(Y) and MU.Untreated(N).
> Similarly MU.Heathshock - MU.Noheatshock(C).
>
> Since affy based estrogen data example is highlited in the
> documentation, I am not sure if the following code is correct or
> writing a design for this design is more complex that what I have
> attempted below.
>
> Method 1:
> --------------
>
>
>> TS <- paste(targets$Cy5,targets$Cy3,targets$Treatment,sep='.')
>>
>
>
>> TS <- factor(TS,levels=c("MU.WT.Y","MU.WT.N","MU.WT.Y","WT.MU.N","M
U.WT.Y","MU.WT.Y"))
>>
>
>
>> TS
>>
> [1] MU.WT.Y MU.WT.N MU.WT.Y WT.MU.N MU.WT.Y MU.WT.Y
> Levels: MU.WT.Y MU.WT.N MU.WT.Y WT.MU.N MU.WT.Y MU.WT.Y
>
>
>> design <- model.matrix(~0+TS,ref='WT')
>>
>
>
>> colnames(design) <- levels(TS)
>>
>
>
>> design
>>
> MU.WT.Y MU.WT.N MU.WT.Y WT.MU.N MU.WT.Y MU.WT.Y
> 1 1 0 0 0 0 0
> 2 0 1 0 0 0 0
> 3 1 0 0 0 0 0
> 4 0 0 0 1 0 0
> 5 1 0 0 0 0 0
> 6 1 0 0 0 0 0
> attr(,"assign")
> [1] 1 1 1 1 1 1
> attr(,"contrasts")
> attr(,"contrasts")$TS
> [1] "contr.treatment"
>
> I am not sure by this method, LiMMA understands dye-swam chips.
>
> Method 2:
> --------------
> Instead, I created a matrix like the following
>
>> mm1 <- read.table('model_matrix1.txt',sep='\t',header=TRUE,stringsA
sFactor=FALSE)
>> mm1 <- as.matrix(mm1)
>> mm1
>>
> Mu.T Mu.NT
> [1,] 1 0
> [2,] 0 1
> [3,] 1 0
> [4,] 0 -1
> [5,] 1 0
> [6,] 1 0
>
>> myFit <- lmFit(MA,mm1)
>> myFit <- eBayes(myfit)
>>
>
> # here Mu.NT is Mutant no treatment.
> method 2 when tried on my data, it works, and I get differentially
> expressed genes but I am not sure if those genes are correct.
>
> Which one of these methods is correct. I am not sure. If both are
> wrong, how can I do this correctly. Please help me.
> thank you.
> Adrian.
>
> _______________________________________________
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> Bioconductor at stat.math.ethz.ch
>
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>
>
Naomi Altman wrote:
> I generally find it easier to do a single channel analysis for these
> more complicated designs. Just follow the manual for single channel
> and you can then use the treatments directly to form the contrasts.
>
> --Naomi
>
> At 12:14 PM 10/31/2008, Adrian Johnson wrote:
>> Hi :
>> sorry for re-sending the my question. Lookinf forward for some
help.
>> thanks.
>> I searched bioconductor list archive and could not find appropriate
>> modelmatrix and contrast matrix fitting
>> for the following type of data from two color.
>>
>>
>> > targets
>> Array Cy3 Cy5 Treatment Shock
>> 1 Array1 WT MU Y H
>> 2 Array2 WT MU N C
>> 3 Array3 WT MU Y H
>> 4 Array4 MU WT N C
>> 5 Array5 WT MU Y C
>> 6 Array6 WT MU Y C
>>
>>
>> I want to compare (MU.WT-Y) - (MU.WT-N)
>> and (MU.WT-H)-(MU.WT-C)
>>
>> How can I design my model matrix and fit contrasts.
>>
>> Could some one help me please.
>>
>> thanks
>> Ad.
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor at stat.math.ethz.ch
>>
https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>>
http://news.gmane.org/gmane.science.biology.informatics.conductor
>
> Naomi S. Altman 814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics 814-863-7114 (fax)
> Penn State University 814-865-1348
(Statistics)
> University Park, PA 16802-2111
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at stat.math.ethz.ch
>
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>
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>
