 

Hi Francesco, 

 

Don’t you think that (for the miRNA data) the quantile method might have the
same problem as the vsn method has, i.e, the assumption that the majority of
the genes do not show differential expression among experimental conditions?
I think that using vsn or quantile method for normalizing between miRNA
chips could introduce strong artifacts in the data. 

 

Pedro.- 

 

 

 

Hi Pedro,

Yes you're right.

I used vsn just cause it transform directly in log2. But I should do with
method="quantile" and than use 
MA<-log2(MA)

or better:

MA <- normalizeBetweenArrays(log2( Gbg.sort$G), method="quantile")

I've just try it and the result are slightly different, apparently it's
better. And in fact in the case of miRNA is good to not make assumptions
about the expression of the majority of the genes. we have 490 x 20 miRNA
plus few hundreds of controls, is really different from the situation of a
microarray for gene expression. 

Thanks for the correction.

Francesco

 

Hi Francesco,

This is not an answer to what you've asked but an additional question. Do
you think that it's correct to apply a vsn normalization for a miRNA chip?.
Vsn assumes that the majority of the genes are not differetially expressed, 
and this might not be the case for a microRNA chip.

Thanks

Pedro.

-----Mensaje original-----
De: HYPERLINK
"mailto:bioconductor-bounces@stat.math.ethz.ch"bioconductor-bounces@stat.mat
h.ethz.ch 
[mailto:HYPERLINK
"mailto:bioconductor-bounces@stat.math.ethz.ch"bioconductor-bounces@stat.mat
h.ethz.ch] En nombre de Francesco
Favero
Enviado el: lunes, 30 de julio de 2007 12:39
Para: HYPERLINK
"mailto:bioconductor@stat.math.ethz.ch"bioconductor@stat.math.ethz.ch
Asunto: [BioC] One-Color Agilent miRNA microarrays

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:

HYPERLINK
"https://stat.ethz.ch/pipermail/bioconductor/2007-May/017203.html"https://st
at.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 h definito
> positivo

(Sorry for the error in Italian...anyway...)
It works if I don't use ndups, but I need this...

I had a look in the lmFit function and it turned out that chol(V) is an 
argument of the gls.series function. And I have the same error if I try to
run gls.series to do my "fit" file.

the only way I managed to do was like this:

fit <- lm.series(MA, design = design, ndups = 20, spacing = 1,weights = 
> Gbg.sort$weights)
> fit$genes <- uniquegenelist(Gbg.sort$genes, ndups = 20, spacing = 1)
> fit$design <- design
> fit$Ameans <- rowMeans (unwrapdups(MA, ndups=20,spacing=1),na.rm=TRUE)
> fit<-new ("MArrayLM", fit)
>

So I've used lm.series instead of gls.series. I don't know if this is an
error or not.
I try to compare the two functions, they are similar, but I'm not sure they 
do exactly the same thing for my case.

Anyway contrasts.fit for time course and eBayes works, and I obtain result
that I'm going to validate with other tools.

Corrections and suggestions are really welcome. 

Thanks for developing an environment like Bioconductor and for all the
support.

Francesco
--

Cancer Genomics Lab.
"Edo e Elvo Tempia" Foundation.
Via Malta 3 13900 Biella
Tel +39 015351830 
HYPERLINK
"http://www.fondoedotempia.it/sub_lab.php"http://www.fondoedotempia.it/sub_l
ab.php

       





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