Hello,

I'd go for Sean's suggestion to include the "Lab" as a factor in the ANOVA 
(in addition to your treatment factors), assuming that the two labs have 
used the same treatments. I'd test whether the treatment factor(s) 
interacts with the "Lab" factor. If there are only few genes with a 
significant interaction, you can assume that the "Lab" does not have a 
major effect on the trend of the treatment effect, only the intercept is 
different. If there are many significant interactions, then there's a 
problem ... :-(

One would assume if only the intercept of the linear model (ANOVA) is 
different that normalizing all chips together will get rid of the 
difference. However, in my experience this is not the case, and I 
recommend to normalize chips from different labs separately.

   hope this helps,
 
   Arne




"Weiwei Shi" <helprhelp@gmail.com> 
Sent by: bioconductor-bounces@stat.math.ethz.ch
22/06/2007 05:12

To
"Kasper Daniel Hansen" <khansen@stat.berkeley.edu>
cc
bioc <bioconductor@stat.math.ethz.ch>
Subject
Re: [BioC] normalization across sites






Then does it mean between-lab variation and between-sample (in one
lab) variation could be treated w/o knowing that? I feel there might
be more than that.

On 6/21/07, Kasper Daniel Hansen <khansen@stat.berkeley.edu> wrote:
> Yes, you could do that.
>
> What Sean is alluding to, is the fact that there is usually a large
> between-lab variation. Some times this variation is greater than the
> biological variation. If you are downloading from a website (or for
> that matter anyway) you should do proper QC of the chips, because you
> will have little idea of what the original people did with it and
> whether they made good choices.
>
> In principle, if the hybes are good and you use proper normalization,
> hopefully you will get results that are comparable from lab to lab.
> But this is not at all given, and you need to be careful when you do
> these things.
>
> Kasper
>
>
> On Jun 21, 2007, at 6:51 PM, Weiwei Shi wrote:
>
> > Ok, if I understand correctly, I should combine two sources of data
> > and run RMA on it?
> >
> > On 6/21/07, Sean Davis <sdavis2@mail.nih.gov> wrote:
> >> Weiwei Shi wrote:
> >>> Hi,
> >>>
> >>> Suppose that I have two sets of arrays, both of which use the same
> >>> platform. My question is, if I have datasets which have been
> >>> preprocessed, what's the best way for me to normalize them.
> >>> Currently
> >>> I just scale them sample-wise; but not sure of it.
> >>>
> >>> Otherwise, should I go back to .CEL file? But how to proceed from
> >>> there?
> >>>
> >> Start from raw data, yes.  You may need to use an ANOVA or linear
> >> model
> >> to deal with confounding variables.  Quality control is especially
> >> important.
> >>
> >> Sean
> >>
> >
> >
> > --
> > Weiwei Shi, Ph.D
> > Research Scientist
> > GeneGO, Inc.
> >
> > "Did you always know?"
> > "No, I did not. But I believed..."
> > ---Matrix III
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives: http://news.gmane.org/
> > gmane.science.biology.informatics.conductor
>
>


-- 
Weiwei Shi, Ph.D
Research Scientist
GeneGO, Inc.

"Did you always know?"
"No, I did not. But I believed..."
---Matrix III

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