[BioC] Spatial Correction

[Justin Borevitz]

It is adhoc and I’ve only thought a little about how to do regional
correction more systematically (the way field studies correct for wet/soil
spots locally effecting growth).  Something like the xy pos of the feature
is in the model for differential expression.  This would be quite
computational and normalization wouldn’t come in the standard way either.

So bg.correct from RMA is not spatial correction. It doesn’t consider the XY
pos.  It does a global per array correction for each array, but assumes
normal noise and exponential RNA signal.  Ben Bolstad was going to model
normal noise and normal signal for DNA, but I don’t think he has done it
yet, no pressure Ben...  We might be in lower demand however SNP arrays
might benefit tremendously from normal/normal. SNP arrays do have their own
correction method with the oligo package though and I’m not sure if anyone
tired this for RNA or if it makes sense even.

Yes quantile normalization is the next step and good luck with the maize
SFPs, if you use whole genome random labeling I guess it will be noisily but
possible with enough replicates.  The deletions sure should be.  Otherwise a
complexity reduction eg AFPL etc might help, see our Barley paper
http://genomebiology.com/2005/6/6/r54


-----
Justin Borevitz
http://naturalsystems.org/lab 
________________________________________
From: Michael Gore [mailto:mag87 at cornell.edu] 
Sent: Friday, October 13, 2006 6:08 PM
To: 'Justin Borevitz'
Subject: Spatial Correction 

Hi Justin,
 
I have a quick question for you.  How does one determine the appropriate
array size and filter size for spatial correction?
 
Is spatial correction comparable to RMA, followed by Quantiles? 
 
Right now, we are doing SFP with the Affy Maize GeneChip.
 
Thanks,
 
Mike
 
 
library(affy)
read.cel <- function(cel.file, cel.image = F, spatial.correct=T, median=F,
                                   array.size = 712, filter.size = 51,
jpeg.save=T){