[BioC] justGCRMA normalisation options?
Arne.Muller at aventis.com
Arne.Muller at aventis.com
Fri Mar 19 18:36:30 MET 2004
Hi,
For affymetrix the stringest differences occure during to the RNA extraction
and purification steps - at least that's my experience. I'm trying to analyze
3 dataset that "should" be the same (same tratment) but have been generated
with slightely different purification kits. The data distributions (visually
inspected kernel density plots) are quite different.
I guess 'vsn' might be a better choice for when there are strong differences
in the distributions since it takes into account the variance effects, e.g.
one experimental batch might have higher intensities due to a better
amplification.
Anyway, it's just a suggestions. Might be realy good if different
normalisation methods could be passed to gcrma or justRMA.
regards,
Arne
--
Arne Muller, Ph.D.
Toxicogenomics, Aventis Pharma
arne dot muller domain=aventis com
> -----Original Message-----
> From: bioconductor-bounces at stat.math.ethz.ch
> [mailto:bioconductor-bounces at stat.math.ethz.ch]On Behalf Of
> Ben Bolstad
> Sent: 19 March 2004 17:37
> To: Andrew Harrison
> Cc: bioconductor at stat.math.ethz.ch
> Subject: Re: [BioC] justGCRMA normalisation options?
>
>
> > Will the fast version have options for normalisation
> > other than the default quantiles? QN is good for merging replicates,
> > which should all have the same distribution. However, it introduces
> > artifacts when comparing chips from different conditions which are
> > observed to have different distributions.
>
> Almost every sophisticated normalization method would
> introduce this same
> "artifact" to one degree or another. However, what I have typically
> observed is that great differences in distribution between
> conditions are
> more often then not due to technical differences (often
> confounded with
> the conditions since people typically do not seem to randomize).
>
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