[BioC] Suitability of normalizeBetweenArrays? - arrays with very different characteristics

[Gordon Smyth]

Dear John,

You must have a mistake somewhere, because 
normalizeBetweenArrays(method="Aquantile") will make no difference to 
results from lmFit(). lmFit() analyses the M-values only, which are 
not affected by the Aquantile normalization method.

Best wishes
Gordon

>[BioC] Suitability of normalizeBetweenArrays? - arrays with very 
>different characteristics
>John Fowler fowlerj at science.oregonstate.edu
>Tue Apr 24 09:08:13 CEST 2007
>
>HI Gordon,
>thank you for the reply.
>
>Any advice in this situation as to the use of Aquantile normalization
>(vs. no between arrays normalization) on the data set when analyzing
>it using lmFit in the two channel situation?  I have done the analysis
>both with and without the Aquantile, and I end up with more significantly
>different probes =with= Aquantile.
>
>regards,
>John
>
>
>Gordon Smyth <smyth at ...> writes:
> > Dear John,
> >
> > You do need to normalize between arrays if you want to do a single
> > channel analysis. It is true that normalization is more difficult if
> > there are very large differences between samples, but you have to do
> > the best you can. I personally use Aquantile in most cases, based on
> > unpublished studies in my own group.
> >
> > Best wishes
> > Gordon
>
> > >From: John Fowler <fowlerj at ...>
> > >Subject: [BioC] Suitability of normalizeBetweenArrays? - arrays 
> with very different characteristics
>
> > >Hello all,
> > >
> > >I have a 3x2 loop design, with three different developmental stages & two
> > >different genotypes, four replicates each.  I am using spotted long oligo
> > >arrays, two colors.  Because the two genotypes have very few expression
> > >differences between them, but two of the developmental stages appear
> > >to be VERY
> > >different, the results on my arrays are also different.  On arrays
> > >in which the
> > >same developmental stage, but different genotypes, are used, the data are
> > >primarily clustered around M=0, distributed along the A axis.  And,
> > >as you would
> > >expect, when the very different developmental stages of the same genotype
> > >used, the amount of variation in M (and presumably in A, as well, although
> > >that's more difficult to see) is obvious in the plots.
> > >
> > >So, is it in-advisable to use normalizeBetweenArrays in this 
> case?  My best
> > >guess as to the most appropriate method to use would be "Aquantile", but I
> > >unsure of that, as well.
>
> > >thank you very much,
>
> > >John Fowler
> > >Associate Professor
> > >Oregon State University