[BioC] Agilent Arrays

Naomi Altman naomi at stat.psu.edu
Mon Jun 27 05:28:24 CEST 2005

You have missed my point about the 50 control spots.  These are all the 
SAME oligo.
The correlation here is induced by the array, not by the RNA concentration, 
hybridization efficiency, etc.

The reason that the two color analysis is supposed to be more efficient 
than 1 channel is the positive correlation between the
errors for the 2 channels on the same spot.  If the channels are 
uncorrelated, then there is no spot effect and using the differences is no
better than using the 2 channels.

The single channel analysis can be used, providing that you use a linear 
mixed model that includes a random effect for array (and, in the case of 
multiple spots per gene) for


At 09:20 PM 6/26/2005, Michael Kirk wrote:
>While I agree that it is probably a bad idea to use single channel
>analysis on two colour arrays, some of the arguments presented here
>are a little troubling.
>The observation that the intra slide correlation is 0.8 doesn't, to my
>mind, show anything unless it is high relative to inter slide
>correlation. Regardless of what treatments are applied to the samples,
>all mouse (say) samples would be expected to have roughly similar
>(array wise) expression profiles. This is partly a reflection of the
>fact that many genes may not vary between treatments and different
>probes will have different hybridization efficiencies (i.e. some spots
>will always have low intensities and some high).
>Secondly, IF the single channel intensities were in fact highly
>accurate, then it is the two colour analysis that would be inefficient
>(in terms of number of arrays required). The two colour idea is
>essentially to overcome noise, particularly noise due to variation in
>the printed spots between slides (i.e. the chemical/physical
>properties of a spot for a given gene may vary between slides).  In
>this case the variation is assumed to affect each hybridized sample
>similarly (multiplicatively) and by taking the ratio this variation is
>removed. A fine idea, but it does leave us with less information than
>if the slide quality was sufficient for this to to be unnecessary.
> >From the two colour analysis of a single slide we have a set of
>ratios, which may then be compared between slides. From the single
>channel analysis of a two colour hybridization we have two sets of
>measurements, which also may be compared between slides.
>With two colour analysis, only three samples can be compared using two
>slides, whereas if the single channel analysis was justified (and note
>I am not say it is, only discussing the arguments given against it),
>then four samples can be compared.
> > 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
>Bioconductor mailing list
>Bioconductor at stat.math.ethz.ch

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

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