Dear Heidi,

thanks for the quick reply,

after traceback() I get

traceback()
5: stop("need at least four unique 'x' values")
4: smooth.spline(ref[i.set], data[i.set])
3: FUN(newX[, i], ...)
2: apply(data, 2, normalize.invariantset, ref = ref.data)
1: normalizeCtData(raw.cat, norm = "scale.rank")

information about the session
sessionInfo()
R version 2.11.1 (2010-05-31)
i386-apple-darwin9.8.0

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] statmod_1.4.8      HTqPCR_1.2.0       limma_3.4.4
RColorBrewer_1.0-2 Biobase_2.8.0

loaded via a namespace (and not attached):
[1] affy_1.26.1           affyio_1.16.0         gdata_2.7.2
gplots_2.8.0          gtools_2.6.2          preprocessCore_1.10.0

On Fri, Jan 21, 2011 at 5:41 PM, Heidi Dvinge <heidi@ebi.ac.uk> wrote:

> Dear Andreia,
>
> > Dear all,
> >
> > I am analysing qPCR data from the Exiqon where I have one card per
> sample,
> > in each card I have one observation for each miRNA. I have in total 8
> > cards,
> > 2 for treatment 1, 3 for treatment 2 and 3 for treatment 3. Each card has
> > one endogenous gene, which I wouldn't like to use to normalize Ct values
> > because is being affected by the type of treatment. So I would like to
> use
> > scale.rank.
> > I am getting the following error:
> >
> > sr.norm <- normalizeCtData(raw.cat, norm = "scale.rank")
> > Error in smooth.spline(ref[i.set], data[i.set]) :
> >   need at least four unique 'x' values
> >
> It sounds like there aren't enough rank-invariant genes across your 8
> cards. If that's the case, then this is admittedly not the most useful
> error message, and it should be changed. What does it say when you run
> traceback() following the error?
>
> The parameter "scale.rank.samples" in normalizeCtData() will let you set
> how many of the samples each gene has to be rank-invariant across in order
> to be excluded. Per default this is the number of samples-1. You can try
> lowering that number, although keeping in mind that the lower it is, the
> less robust your resulting rank-invariant genes are. If your samples are
> all highly variable across all genes, it might not be possible for you to
> use this normalisation method.
>
> If this does not seem to be the problem, something else might be going on
> with the function. In that case, please report back here and I can perhaps
> have a look at your data.
>
> I have been considering adding an additional parameter to normalizeCtData,
> so that genes just have to be rank-invariant within a certain interval,
> e.g. be located within -/+5 of each other on the ranked list. For rather
> low-throughput qPCR cards that could mess things up though.
>
> HTH
> \Heidi
>
> > Does this mean I don't have enough replicates?
> >
> > thanks for the help
> >
> > Andreia
> >
> > --
> > --------------------------------------------
> > Andreia J. Amaral
> > Unidade de Imunologia Clínica
> > Instituto de Medicina Molecular
> > Universidade de Lisboa
> > email: andreiaamaral@fm.ul.pt
> >           andreia.fonseca@gmail.com
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> > http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>
>


-- 
--------------------------------------------
Andreia J. Amaral
Unidade de Imunologia Clínica
Instituto de Medicina Molecular
Universidade de Lisboa
email: andreiaamaral@fm.ul.pt
          andreia.fonseca@gmail.com

	[[alternative HTML version deleted]]

