[BioC] Options for spatial normalization?

[Oliver Homann]

Hello,

I was wondering if anyone could offer me some advice on the best 
approach for normalizing my two-color expression arrays.  I will be 
processing a large number of arrays, and ideally I would like to develop 
a semi-automated normalization pipeline.  Some of my arrays have issues 
with spatial effects, and currently the only method that I'm aware of 
for dealing with such effects is in the Maanova package (the "rlowess" 
method of transform.madata).  However, this method is far from ideal for 
my purposes because it utilizes grid layout rather than 'X' and 'Y' 
positions to calculate proximity (which causes some problems with gaps 
between blocks) and because it is coupled to a intensity-based 
normalization (which limits the flexibility somewhat). 

I have a few specific questions:
[1] Are there any other methods for spatial normalization of two-color 
data implemented in R?
[2] In my attempts to develop a normalization pipeline I have been 
stymied by the need to ascertain on a slide-by-slide basis which types 
of normalization are needed (e.g. pin/intensity/spatial).  Do any of you 
have a "rule-of-thumb", or better yet a quantitative approach to making 
this decision?

Thanks!
Oliver Homann