[BioC] Agilent Arrays

[Michael Kirk]

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.

Michael

> 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

[snip]