[BioC] One-Color Agilent miRNA microarrays

[Gordon K Smyth]

> Date: Mon, 30 Jul 2007 12:39:19 +0200
> From: "Francesco Favero" <favero.francesco at gmail.com>
> Subject: [BioC] One-Color Agilent miRNA microarrays
> To: bioconductor at stat.math.ethz.ch
> Message-ID:
> 	<794f7b0e0707300339k5028abcscdc9ea2eca18d334 at mail.gmail.com>
> Content-Type: text/plain
>
> Dear all,
>
> I'm working with new microRNA one-color microarray from Agilent.
> I decided to use limma, and in this particular case, I needed to perform a
> time-course experiment.
>
> Thanks to this list I've done quite everything, but I'm not sure it's
> perfectly right...
>
> I've imported the chips thanks to Peter White and Dr. Gordon Smyth:
>
> https://stat.ethz.ch/pipermail/bioconductor/2007-May/017203.html
>>
>
> So I have a G data with green and dummy red intensity.
>
> The normalisation:
>
> Gbg<-backgroundCorrect(G, method="subtract")
>>
>
> This array have 20 spots for each microRNA but they don't have an order in
> the chip, so I putted them in order for GeneName, so I'll able to use the
> ndups argument:
>
> spottypes<-readSpotTypes()
>> G$genes$Status<-controlStatus(spottypes,G)
>> Gbg.subset <- Gbg[Gbg$genes$Status == "Gene",]
>> Gbg.sort <- Gbg.subset[order(Gbg.subset$genes[,"GeneName"]),]
>>
>
> Again normalisation between array just the green channel:
>
> MA <- normalizeBetweenArrays(Gbg.sort$G, method="vsn")
>>
>
> And performed a normal time course experiment as from the limma manual:
>
> lev <- c("15", "37", "97", "167", "618")
>> f <- factor(targets$Cy3, levels=lev)
>> design <- model.matrix(~0+f)
>> colnames(design) <- levdupcor <- duplicateCorrelation(MA,design,ndups=20,
>> spacing=1)
>>
>
> now I have to fit everything in the linear model... but lmFit doesn't work.
> It complains an error on chol(V).
>
> fit <- lmFit(MA,design,ndups=20,spacing=1,correlation=dupcor$consensus)
>> Errore in chol(V) : il minore principale di ordine 2 non è definito
>> positivo
>
> (Sorry for the error in Italian...anyway...)
> It works if I don't use ndups, but I need this...

What sort of object is MA?  Is it just a matrix, or is it a MAList?

What version of limma?

In any data analysis, you should check you results at every step.  For example, have you looked at
your normalized data?  What is the value of corfit$consensus?

Best wishes
Gordon