[BioC] Agilent Arrays

Fri Jun 24 16:36:48 CEST 2005

> Basically, you're saying that if the arrays are very high quality, you can
> get away with an inefficient analysis.

Gordon, I did not say that, it sounds stupid, please do not misquote
people.

> Naomi is refering to what I call the "intraspot" correlation, see for
> example the intraspotCorrelation() function in the limma package, and it
> is critically important. The correlation isn't a bad thing, nor is it
> restricted to poor quality arrays. Rather it means that contrasts
> estimated within a spot are highly accurate.

I agree that contrasts estimated from within one array are more
accurate than those from different arrays. Note that when I said
"treat a two-color array like two single-color arrays", this was in
the paragraph on how to normalize, not on differential expression. But
apparently this still triggered off a few people ...

Two aspects were raised by Claus' question that started this thread:
how to normalize these data, and how to identify differentially
expressed genes.  My experience is that multi-channel normalization
methods like vsn (or quantiles for that matter) work well for sets of
mass-produced two-color arrays. Then, it is still better to look at
contrasts within arrays. But it is at least possible (even if less
accurate / precise) to look at contrasts across arrays by directly
comparing the intensities, rather than always having to go through a
chain of log-ratios.

> Why not do it properly and get the full benefit of the high
> quality arrays? My experience is that high quality
> Agilent arrays can beat affy for accuracy if treated properly.

Agreed. Do you think it's because of the two colors or of the longer
(and hence more specific) probes ?

Best wishes
 Wolfgang

<quote who="Gordon Smyth">
> Wolfgang,
>
> Naomi is refering to what I call the "intraspot" correlation, see for
> example the intraspotCorrelation() function in the limma package, and it
> is
> critically important. The correlation isn't a bad thing, nor is it
> restricted to poor quality arrays. Rather it means that contrasts
> estimated
> within a spot are highly accurate. It is what makes the two-colour
> technology intrinsically more accurate than one channel technology, other
> things being equal. See http://www.statsci.org/smyth/pubs/ISI2005-116.pdf
> for some discussion.
>
> Basically, you're saying that if the arrays are very high quality, you can
> get away with an inefficient analysis. Why not do it properly and get the
> full benefit of the high quality arrays? My experience is that high
> quality
> Agilent arrays can beat affy for accuracy if treated properly.
>
> Gordon
>
>>Date: Thu, 23 Jun 2005 15:29:38 +0100 (BST)
>>From: "Wolfgang Huber" <huber at ebi.ac.uk>
>>Subject: Re: [BioC] Agilent Arrays
>>To: "Naomi Altman" <naomi at stat.psu.edu>
>>Cc: bioconductor at stat.math.ethz.ch
>>
>>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
>
>

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