[BioC] Options for spatial normalization?

[Jay Konieczka]

Hi Olivier,

Take a look at the OLIN package.  It takes xy coordinates and uses a  
machine learning approach to approximate the smoothing parameters for  
spatial and intensity normalization.  I have the same issue and I've  
had a great deal of success with it.  I wouldn't recommend bypassing  
the slide-by-slide oversight, but you may find a set of parameters  
supplied to OLIN that is sufficient for the overwhelming majority of  
your chips.

Cheers,

jay


On Mar 7, 2007, at 11:47 AM, Oliver Homann wrote:

> 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
>
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