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

["Gläßer, Christine"]

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?


Best wishes,


Christine
-----------------------------------------------------------------------
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
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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