[BioC] LIMMAing normalized and background corrected MA-data derived by a text file

["Gläßer, Christine"]

Dear mailing-list,

I just want to inform you that the problem is solved. It was a simple input error; I swapped a dye-swap back without realizing it in a first, preparatory formatting step, leading to wrong targets. Actually, on one array the values of the red signal were not, as the targets stated, derived by the mutant line, but the control, which lead to highly unsignificant p-values.

So, I used the script as previously described:

library(limma)
tfile <- "Targets.txt"
outfile <- "firstrun.txt"
targets <- readTargets(tfile)
RG <- read.maimages(targets, columns=list(G="green",R="red"), annotation=c("Block","Row","Column","Name","ID"))
RG$printer <- getLayout(RG$genes) ### optional at last
RG_ma <- normalizeBetweenArrays(RG, method="none") ### as previously suggested; converting the input into a MAList for subsequent manipulation without any corrections

E.avg <- avereps(RG_ma, ID=RG_ma$genes$ID)

design <- modelMatrix(targets, ref="control")
fit <- lmFit(E.avg, design)
fit2 <- eBayes(fit)

sink(outfile, options(max.print=5.5E5))
print ("Mutant-Control")
print (topTable(fit2, coef=1, number=50000, adjust="BH"))
sink()

using previously formatted input files containing block, row and column.

Thanks, and best regards,


Christine Gläßer
-----------------------------------------------------------------------
Christine Gläßer
Institut für Bioinformatik und Systembiologie
Tel.: +49-(0)89/31873583
________________________________________
Von: seandavi at gmail.com [seandavi at gmail.com] im Auftrag von Sean Davis [sdavis2 at mail.nih.gov]
Gesendet: Freitag, 17. Juni 2011 12:25
An: Gläßer, Christine
Cc: Yong Li; bioconductor at r-project.org
Betreff: Re: [BioC] LIMMAing normalized and background corrected MA-data derived by a text file

On Fri, Jun 17, 2011 at 6:06 AM, "Gläßer, Christine"
<christine.glaesser at helmholtz-muenchen.de> wrote:
> Dear Yong,
>
> thank you very much for your response. I proceeded like you suggested:
>
> RG_ma <- normalizeBetweenArrays(RG, method="none")
>
> then averaged the replica (which are not spotted regularly)
>
> E.avg <- avereps(test, ID=RG_ma$genes)
>
> and fitted the values
>
> design <- modelMatrix(targets, ref="control")
> fit <- lmFit(E.avg, design)
> fit2 <- eBayes(fit)
>
> However, no gene is significantly differentially expressed (adj.p-value 0.99xxx for each gene, BH), which cannot be true (compared to results calculated with CybRT (http://cybert.ics.uci.edu/help/index.html); some genes missing would be reasonable, but all genes being not significantly diff. expressed?). Do you have any suggestions what is going wrong?
>

We do not know what "targets" or "design" above look like.  Perhaps
you could share those?

Sean


> -----------------------------------------------------------------------
> Christine Gläßer
> Institut für Bioinformatik und Systembiologie
> Tel.: +49-(0)89/31873583
> ________________________________________
> Von: Yong Li [yong.li at zbsa.uni-freiburg.de]
> Gesendet: Freitag, 17. Juni 2011 11:28
> An: Gläßer, Christine
> Cc: bioconductor at r-project.org
> Betreff: Re: [BioC] LIMMAing normalized and background corrected MA-data derived by a text file
>
> Dear Christine,
>
> the function read.maimages gives you a RGList. To convert RGList to
> MAList, the functions normalizeBetweenArrays can be used. You can use
> method="none" when calling the function to omitting any normalizations.
> For more details type help(normalizeBetweenArrays) in your R session.
>
> Best regards,
> Yong
>
> Gläßer, Christine wrote:
>> Dear all,
>>
>> I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in:
>>
>> MA <- read.maimages(targets,  columns=list(G="mutant",R="control"), annotation=c("Name", "ID"))
>>
>> Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data?
>>
>> Best regards, and thank you,
>>
>>
>> Christine Gläßer
>>
>>
>> -----------------------------------------------------------------------
>> Christine Gläßer
>> Institute of Bioinformatics and Systems Biology
>>
>> Helmholtz Zentrum München
>> Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
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>
> Helmholtz Zentrum München
> Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
> Ingolstädter Landstr. 1
> 85764 Neuherberg
> www.helmholtz-muenchen.de
> Aufsichtsratsvorsitzende: MinDir´in Bärbel Brumme-Bothe
> Geschäftsführer: Prof. Dr. Günther Wess und Dr. Nikolaus Blum
> Registergericht: Amtsgericht München HRB 6466
> USt-IdNr: DE 129521671
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor at r-project.org
> https://stat.ethz.ch/mailman/listinfo/bioconductor
> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
>

Helmholtz Zentrum München
Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)
Ingolstädter Landstr. 1
85764 Neuherberg
www.helmholtz-muenchen.de
Aufsichtsratsvorsitzende: MinDir´in Bärbel Brumme-Bothe
Geschäftsführer: Prof. Dr. Günther Wess und Dr. Nikolaus Blum
Registergericht: Amtsgericht München HRB 6466
USt-IdNr: DE 129521671