[BioC] Agilent Arrays

[Wolfgang Huber]

Hi Naomi,

and why is that important? Also, what is the within gene correlation
between green foreground of array 1 and green foreground of array 2?

Bw
 Wolfgang

<quote who="Naomi Altman">
> I am working with Agilent arrays on which we have spotted many replicates
> of the control spots.
> The within gene correlation between red and green forground is about 0.8
> for the unnormalized data - i.e. pretty high!
>
> --Naomi
>
> At 03:23 AM 6/23/2005, Wolfgang Huber wrote:
>>Hi Claus,
>>
>>for the normalization of arrays where the spotting etc. variability
>>between chips is not strong, you can treat the data from m two-colour
>>arrays as if it were 2*m single colour ones, and use methods like
>>"quantiles" or "vsn".
>>
>>Note that for almost all genes, the hybridization is not limited by the
>>amount of probe DNA, hence the competition between red and gree target is
>>negligible for almost all genes (execept possibly the most highly
>>expressed ones). This justifies treating a two-color array like two
>>single-color arrays.
>>
>>Only later when you consider the contrasts of interest for finding
>>differentially expressed genes, you want to make sure that these are not
>>confounded with dye.
>>
>>PS, I think your question is very directly Bioconductor related!
>>
>>Best wishes
>>   Wolfgang
>>
>>
>><quote who="Claus Mayer">
>> > Dear all!
>> >
>> > Apologies for asking a question which is not directly Bioconductor
>> > related: After some experience with spotted 2-channel arrays and
>> > Affydata, I am currently analysing my first data set based on Agilent
>> > arrays. I know that packages like marray or limma have facilities to
>> > read these data and that they can be normalised and analysed like any
>> > other 2-colour-arrays. On the other hand the printing technology of
>> > these arrays (using inkjet-printing of 60mer oligos) is closer in
>> spirit
>> > to Affy, if I understand this correctly. This seems to show in the
>> data
>> > as well. For example the strongest correlations I found in the single
>> > channel (log-)intensities was not between the two channels observed on
>> > the same slide (like with spotted arrays), but between the two
>> channels
>> > (differently dyed on different arrays in a loop design) that contained
>> > the same sample (which is quite reassuring). This made me wonder
>> whether
>> > (once dye and array effects have been removed by some normalisation
>> > method) with Agilent arrays one might really use single channel
>> > intensities as measures of gene expression instead of reducing them to
>> > the log-ratio only as is usually done for two-channel data.
>> >
>> > This would have consequences on the way these arrays should be
>> > normalised (rather by a multichip method than individually) and also
>> > allow more flexibility in the design of experiments.
>> >
>> > As I said before this is my first Agilent data set, so I would be
>> > interested to hear opinions of others with more experience. Before I
>> > start to re-invent the wheel here, I’d be also interested to know
>> > whether any of you is aware of tools, software, papers, etc
 dealing
>> > with the analysis of Agilent array data specifically (rather than just
>> > applying standard methods for 2-coloured cDNA -arrays).
>> >
>> > Any help/comments appreciated
>> >
>> > Claus
>> >
>> > --
>> >
>> ***********************************************************************************
>> >  Claus-D. Mayer                       | http://www.bioss.ac.uk
>> >  Biomathematics & Statistics Scotland | email: claus at bioss.ac.uk
>> >  Rowett Research Institute            | Telephone: +44 (0) 1224 716652
>> >  Aberdeen AB21 9SB, Scotland, UK.     | Fax: +44 (0) 1224 715349
>> >
>> > _______________________________________________
>> > Bioconductor mailing list
>> > Bioconductor at stat.math.ethz.ch
>> > https://stat.ethz.ch/mailman/listinfo/bioconductor
>> >
>> >
>>
>>
>>-------------------------------------
>>Wolfgang Huber
>>European Bioinformatics Institute
>>European Molecular Biology Laboratory
>>Cambridge CB10 1SD
>>England
>>Phone: +44 1223 494642
>>Http:  www.ebi.ac.uk/huber
>>
>>_______________________________________________
>>Bioconductor mailing list
>>Bioconductor at stat.math.ethz.ch
>>https://stat.ethz.ch/mailman/listinfo/bioconductor
>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Bioinformatics Consulting Center
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
>
>


-------------------------------------
Wolfgang Huber
European Bioinformatics Institute
European Molecular Biology Laboratory
Cambridge CB10 1SD
England
Phone: +44 1223 494642
Http:  www.ebi.ac.uk/huber