Hi,
 
I'm working with some collaborators on a Diffuse Large B-Cell Lymphoma
(DLBCL) dataset. They have data on Success and Failure of patients after
treatment from 20 Affy HGU133plus2 chips processed in one lab (their
'training set') and 47 HGU133plus2 chips processed in another lab (their
'validation set'). There were some minor protocol differences between
labs (the first lab made the hyb buffers from scratch, the second used
Affy's pre-made kit).
 
They are interested in deriving a gene signature from the first data
that they can test for predictive value on the second set. Never mind
for a moment the small size of their training set, or the issue of
whether 'training' and 'validation' sets are the best way of organizing
data...
 
All efforts so far have been fruitless. We have used both the
geSignatures and the StabPerf packages to derive signatures, and
globaltest to test predictive power. 
 
Perhaps related to this failure, it turns out that though there is a
certain minor level of significant changes in gene expression between
Success and Failure from within each Lab grouping (above the FP rate),
there is a HUGE # of significant differences between the two labs when
the data for each lab is pooled. This indicates that the Lab axis of
variability is much more important than the difference between Success
and Failure.
 
Are there any already established normalization techniques that would
allow us to 'remove' the effect of Lab from the dataset? We thought of
making the mean of each probeset in Lab 1 equal to the mean of the same
probeset in Lab 2, to try and linearly remove any systematic errors
while preserving inter-gene relationships within each Lab. Any
suggestions?
 
Many thanks,
 
Tarun Nayar
Genome Sciences Centre
Vancouver, BC

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