[BioC] Affymetrix quality control and normalization

[laurent buffat]

Hi,

For the quality control

Look at affyPLM package and plot the residual of a linear probe model :

And look the "pseudo-image". If the residual are randomly distributed, when
your hybridation is probably good.

Hopes it's help.

L. Buffat



-----Message d'origine-----
De : bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] De la part de Dipl.-Ing.
Johannes Rainer
Envoyé : mardi 27 juillet 2004 08:36
À : James MacDonald
Cc : bioconductor at stat.math.ethz.ch
Objet : [BioC] Affymetrix quality control and normalization

hi,

we got a new affymetrix station and are now becoming a affymetrix core
facility
. as i am fairly new in the one color micro array field i wanted to know how
other people work with affymetrix chips.
at the moment i am normalizing the chips with RMA. i compared these results
with
MAS5 and GC-RMA background correction. from this comparsions it looked that
RMA
worked best (also with only two chips used in the comparsion), GC-RMA made
some
strange adjustements (i found genes down regulated after GC-RMA background
correction, where they should (must) be up regulated). with MAS 5 i get to
many
regulated genes, to big variance in the low intensity range... so the method
i
am using now is RMA.
now to the questions:

a) quality control: how to define when a chip has not worked, when excluding
a
chip from the analyis? i am looking at the moment at the histogram (big
signal
range or not?) and at the 3'  5' ratio, but where is the limit for this
range?
when was the RNA degraded?

b) RMA with a low number of chips, is this possible? i thins the more chips
(biological replicates) i have the better the model fitting workes.

c) defining regulated genes: i am currently using a M (fold change) cut off
of 1
(2 fold), better solutions?

thanks, jo

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