Answer: Combining HGU133A & HGU133B data
A more serious issue is that normalisation (almost certainly) assumes
that the average expression level on each chip is the same. This is
clearly not the case between A and B chips - and combing each pair of
A's and B's for every sample, before normalisation, is almost
certainly a bad idea...
Normalising the A's and B's separately is probably much more sensible
- and this then allows you to use the 2000+ shared probes to see how
well your normalisation has worked: their signals are from the same
hyb. cocktail so they should produce the same expression levels. If
you think about it this way, the repeated probes are a Good Thing(TM)
> -----Original Message-----
> From: Adaikalavan RAMASAMY [mailto:firstname.lastname@example.org]
> Sent: 15 September 2003 11:26
> To: email@example.com
> Cc: Mark.Reimers@biosci.ki.se
> Subject: [BioC] Combining HGU133A & HGU133B data
> Dear all,
> I have been asked to analyze the data where samples were hybridized
> both HGU133A and HGU133B Affymetrix chips. One option is to
> analyze the
> A and B chips seperately but this is not desirable.
> The other option is to combine both (using something akin to
> "rbind") to
> combine these data. I think it is better to combine the results
> rma as different background correction needs be applied.
> This method however does have its problems with the genes redundant
> between A and B chip (there are 2000+ genes that overlap both
> Can anyone suggest what is the best way to deal with this
> problem ? Does
> anyone have any experience or seen publications combining
> data from two
> different array formats.
> Thank you.
> Adaikalavan Ramasamy
> Research Assistant
> Microarray & Expression Genomics Tel: 65-6478 8043
> Information & Mathematical Sciences Fax: 65 6478 9058
> Genome Institute of Singapore
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