[BioC] One-color spotted microarrays analysis

[Henrik Bengtsson]

Suggestion: Your scanner probably shift your data by a small constant
(~15-25 => significant impact) due to what I call "scanner offset"
(Bengtsson et al. 2004).  This offset tends to be stable across scans
(arrays).  Try to correct for this single(!) parameter before anything
else.  This will simplify your life and safe you many hours.  You can
either estimate it by assume it is there or estimate it using a
multiscan protocol as suggested in the above reference.   Methods are
available in aroma.light.  Then follow what has already been suggested
by others - often things are rather linear afterward so simple
rescaling (or linear regression) will take you quite far.

H. Bengtsson, J. Vallon-Christersson and G. Jönsson, Calibration and
assessment of channel-specific biases in microarray data with extended
dynamical range, BMC Bioinformatics, 5:177, 2004.

My $.02

/Henrik

On Tue, May 26, 2009 at 3:22 PM, Wolfgang Huber <huber at ebi.ac.uk> wrote:
> Hi Pier-Luc
>
> here's one more option: the "strata" parameter of vsn2 (in the vsn package)
> allows fitting the normalisation model separately for (print-tip) groups of
> probes, for an arbitrary number of one-colour arrays.
>
> If non-linear regression is desired, the method described in [1] is less ad
> hoc and less clumsy than loess against a "virtual second channel".
>
> [1] Normalization and analysis of DNA microarray data by self-consistency
> and local regression, TB Kepler, L Crosby, KT Morgan - Genome Biol, 2002.
>
>  Best wishes
>        Wolfgang
>
> Naomi Altman ha scritto:
>>
>> 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.
>>
>> --Naomi
>>
>> 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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>
>
> --
>
> Best wishes
>     Wolfgang
>
> ------------------------------------------------
> Wolfgang Huber, EMBL, http://www.ebi.ac.uk/huber
>
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