[BioC] process one color microarray

[James W. MacDonald]

Hi Paz,

On 9/18/2011 11:20 AM, Paz Tapia Ramirez wrote:
> Hi JIM.
> When you say"trt1" "trt2" you mean the column GErep in the targets file ?

I don't know anything about your targets file, as you didn't show us 
what that looks like.

And as Yong noted, even though you claim a one-color array, you appear 
to be doing something other than a conventional one-color analysis. So 
first you need to clarify exactly what you are trying to do with this 
analysis, what kind of data these are, what your targets file looks 
like, etc.

Best,

Jim

>
>  > Date: Fri, 16 Sep 2011 13:32:30 +0200
>  > From: yong.li at zbsa.uni-freiburg.de
>  > To: jmacdon at med.umich.edu
>  > CC: verotapia at alumnos.utalca.cl; bioconductor at r-project.org
>  > Subject: Re: [BioC] process one color microarray
>  >
>  > Hi Paz, hi Jim,
>  >
>  > I think there is a problem here. Jim, you have given detailed
>  > explanation of making design matrix and do various comparisons, but they
>  > are based on the assumption that the input given to lmFit is log
>  > expression values from one color array. The problem is that, based on
>  > Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to
>  > processed signals of the green channel (of course background corrected
>  > and quantile normalized). I doubt very much that that piece of codes
>  > does what Paz wants to do. However, it's also possible that I missed
>  > something.
>  >
>  > Best regards,
>  > Yong
>  >
>  > James W. MacDonald wrote:
>  > > Hi Paz,
>  > >
>  > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote:
>  > >> Hello, I have a question. I'm Working with one-color microarrays . I
>  > >> worked in 4 conditions different and each condition I have 4
>  > >> replicates. Now, my question is when I load the files to
>  > >> Bioconductor, I load as follows:
>  > >> my.filenames<- c ("Condic1_repl1.txt",
>  > >> "Condic1_repl2.txt",
>  > >> "Condic1_repl3.txt",
>  > >> "Condic1_repl4.txt",
>  > >> "Condic2_repl1.txt",
>  > >> "Condic2_repl2.txt",
>  > >> "Condic2_repl3.txt",
>  > >> "Condic2_repl4.txt",
>  > >> "Condic3_repl1.txt"
>  > >> "Condic3_repl2.txt",
>  > >> "Condic3_repl3.txt",
>  > >> "Condic3_repl4.txt",
>  > >> "Condic3_repl1.txt",
>  > >> "Condic3_repl2.txt",
>  > >> "Condic3_repl3.txt",
>  > >> "Condic3_repl4.txt")
>  > >>
>  > >> Subsequently, I realize the normalization procedure and some
>  > >> statistical calculations:
>  > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal"
>  > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground")
>  > >> RG1<- read.maimages (my.filenames, source = "agilent", columns =
>  > >> one.col1, dec =".")
>  > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50)
>  > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile")
>  > >> fit1<- lmFit (MA1, design = NULL)
>  > >
>  > > The design matrix indicates to lmFit() what is control and what is
>  > > treatment. When you specify a NULL design matrix, lmFit() will just
> use
>  > > a vector of 1s, which would be fine if you had two-color chips and no
>  > > dye-swaps. Otherwise, you are just testing the hypothesis that the
>  > > average expression of all samples is not equal to zero (which
> obviously
>  > > isn't correct).
>  > >
>  > > So if you have four conditions with four replicates (and I am assuming
>  > > here that they are Biological replicates, not just different
> aliquots of
>  > > the same sample), you want a design matrix with four columns. The
>  > > simplest such design matrix (to me, anyway), computes the mean
>  > > expression for each group, and then you can just make the comparisons
>  > > you want.
>  > >
>  > > cond <- factor(rep(1:4, each = 4))
>  > > design <- model.matrix(~ 0 + cond)
>  > > colnames(design) <- c("trt1","trt2","trt3","trt4")
>  > >
>  > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2,
>  > > trt4-trt2, trt4-trt3, levels = design)
>  > > fit <- lmFit(Ma1, design)
>  > > fit1 <- contrasts.fit(fit, contrast)
>  > > fit1 <- eBayes(fit1)
>  > >
>  > > which will make all possible comparisons.
>  > >
>  > > Alternatively, if you just want to compare all treatments to a control
>  > > (and assuming your control is trt1).
>  > >
>  > > design <- model.matrix(~cond)
>  > > fit <- lmFit(Ma1, design)
>  > > fit1 <- eBayes(fit)
>  > >
>  > > In this case, all coefficients in the model will be e.g., trt2-trt1,
>  > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly.
>  > >
>  > > Best,
>  > >
>  > > Jim
>  > >
>  > >
>  > >
>  > >> fit1<- eBay (fit1)
>  > >>
>  > >> But my question is: how I can specify to bioconductor which files
>  > >> correspond to Control, or which correspond to microarrays with
>  > >> treatment?
>  > >>
>  > >> Regards, Paz
>  > >>
>  > >> [[alternative HTML version deleted]]
>  > >>
>  > >> _______________________________________________
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>  > >

-- 
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
5912 Buhl
1241 E. Catherine St.
Ann Arbor MI 48109-5618
734-615-7826
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