[BioC] normalize by or across all treatments

[Rafael A. Irizarry]

you;ll probably get conflicting responses here too... in my experience the
risk of inducing effects is much greater than the risk of
diminishing effects. look at unnormalized data from replicate arrays and
you will
see large differences. this means that when you see large differences you
cant be sure if its artifcact/obscuring variation or real biological
variation. if in your experiment you expect global gene expression to be
distributed roughly the same across conditions then quantiles
normalization (the default on rma) will be fine. if you expect most gene
expression no to change across condition then most normalizations
available in the affy package (qsplines, loess, contrasts, etc...) should
work fine as well.

a paper by bolstad et al. in bioinformatics (2003) has some  empirical
results on all this.

On Mon, 16 Jun 2003, Leanna House wrote:

> In replicates of 3, I have a set of control and 4 treatment arrays.  My
> question is, do I normalize (via rma) using all of the arrays at once, or
> do I normalize by treatment.  I have asked other reliable sources and have
> received conflicting responses.  I feel the issue is that, in one case, I
> may, if not completely wipe out, severely diminish any possible treatment
> effects, whereas, in the other case, I may actually induce a treatment
> effect.  Any thoughts?
> 
> Thank you so much,
> Leanna
> 
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