[BioC] Bioconductor Digest, Vol 28, Issue 23
Gordon Smyth
smyth at wehi.edu.au
Fri Jun 24 15:50:49 CEST 2005
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
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