[BioC] One-color spotted microarrays analysis

Naomi Altman naomi at stat.psu.edu
Sat May 23 06:12:46 CEST 2009

I have never done spatial 
normalization.  However, my expertise is 
nonparametric smoothing.  You should not need
a virtual channel to remove the spatial effect, 
because you are regressing on the x,y co-ordinates, not the spot means.

I think this is available in either marray or 
limma.  However, if it is not, you can use the 
loess function to do 2-d regression and
then use the residuals as the expression 
values.  (Usually you would do this after taking 
log2(expression)).  Usually some constant is
added back so that the array mean is not zero but some positive number.


At 07:50 AM 5/22/2009, Pier-Luc Poulin wrote:
>Thank Naomi and Mark for the suggestions. Quantile normalization
>will be used to normalize between arrays but we have a spatial
>problem within arrays.
>Our arrays are custom oligo arrays. We have 48 print-tips on
>the array and there is an intensity bias from top to bottom
>of the arrays. I would like to normalize the spatial distribution
>of intensities between sub-arrays. Can a 2d spatial normalization
>be done without creating a "virtual" channel and using only
>one channel?
>Which package and function of BioConductor should I use?
>P-L Poulin
>Research assistant
>Université Laval
>-----Message d'origine-----
>De : Naomi Altman [mailto:naomi at stat.psu.edu]
>Envoyé : May 21, 2009 11:19 PM
>À : Mark Cowley; Pier-Luc Poulin
>Cc : bioconductor at stat.math.ethz.ch
>Objet : Re: [BioC] One-color spotted microarrays analysis
>I am aware of 2 methods.
>You could do quantile normalization.  This is
>very stringent as it forces the overall
>expression distribution to be the same on every array.
>You could pick one array (or the genewise mean or
>median of all arrays) to act as the "control" and
>lowess normalize everything relative to that array.
>At 09:05 PM 5/21/2009, Mark Cowley wrote:
> >Hi Pier-Luc,
> >What sort of within array normalisation are you trying to perform? ie
> >what issues have you spotted in your data that you think need removing?
> >
> >There are no print-tip's as such on an agilent array since they're
> >printed by fancy ink jet printers.
> >You can't do lowess, unless you make a 'virtual' 2nd channel, perhaps
> >by taking the average of all channels.
> >
> >cheers,
> >Mark
> >-----------------------------------------------------
> >Mark Cowley, PhD
> >
> >Peter Wills Bioinformatics Centre
> >Garvan Institute of Medical Research, Sydney, Australia
> >-----------------------------------------------------
> >On 22/05/2009, at 12:47 AM, Pier-Luc Poulin wrote:
> >
> >>Hi Massimo and everyone,
> >>
> >>Thanks for the suggestion. I've read documentation about
> >>Agi4x44PreProcess
> >>and Affy packages but I still need advice. Agi4x44PreProcess
> >>contains no
> >>method for normalization between print tips within arrays.
> >>
> >>Our data consists of single color spotted chips. We need to find a way
> >>in bioconductor to do within array normalization. Limma's
> >>normalizeWithinArrays
> >>and marray's maNormMain only do that for two color arrays. I haven't
> >>find
> >>any function for normalizing single channel arrays, except for
> >>affymetrix chips.
> >>
> >>Thanks
> >>
> >>P-L Poulin
> >>Research assistant
> >>Université Laval
> >>www.arborea.ca
> >>
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>Naomi S. Altman                                814-865-3791 (voice)
>Associate Professor
>Dept. of Statistics                              814-863-7114 (fax)
>Penn State University                         814-865-1348 (Statistics)
>University Park, PA 16802-2111
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111

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