[BioC] problem with expresso()

[Oliver Hartmann]

Dear lsit memners,

I am trying to find a way of normalzing affy chips with vsn (I found a 
data set where rma() doesn't do well together with the t-statistic and I 
was hopeing that vsn() could fix that). I used the following script:

data <- ReadAffy()
normalize.AffyBatch.methods <- c(normalize.AffyBatch.methods, "vsn")
es = expresso(data,
         pmcorrect.method = "pmonly",
         bgcorrect.method = "none",
         normalize.method = "vsn",
         summary.method   = "medianpolish")

With this, identifying differentially expressed genes works fine 
(results are very similar to rma() - see my tech report for details if 
you like).
But there seems to be one problem: the intensities and the values \delta 
h for differential expression (equivalent to the difference between the 
log-ratios if using rma()) are both on the wrong scale. Well, as rma() 
and other methods use log-transformed data, but vsn() uses a different 
tranformation, I think using expresso() to calculat vsn-normalized 
measures seems to log- AND arcsin-transform the data. Is there a way 
around that? From the description I didn't find a way around 
log-transformation nor where exactly the log-transformation was taking 
place.

If you are interested in the comparission of the performance of rma(), 
vsn() and MAS() tested on affymetrix data with spike in genes you can 
find a tech report at http://staff-www.uni-marburg.de/~hartmann/ - but 
only very preliminary work, sorry.

Thanks a lot

	-oliver hartmann-

-- 
Oliver Hartmann, Institute of Medical Biometry and Epidemiology
Philipps-University Marburg, Bunsenstr. 3, D-35037 Marburg
phone +49(0)6421 28 66514, fax +49(0)6421 28 68921