[BioC] Agilent Arrays

[Naomi Altman]

Lets wait on this one until I have a chance to analyze the data more 
completely.

--Naomi

At 12:37 AM 6/27/2005, Michael Kirk wrote:
>Quoting Naomi Altman <naomi at stat.psu.edu>:
>
> > Kirk,
> > 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.
>
>OK, if it's the same oligo then that does indicate a spot, or hybridization
>effect. Not suprising I guess. Out of curiosity - did you quantify the size
>of the effect? I.e. was the inter replicate within slide variation of 
>significant
>size relative to whatever variation may be expected between treatments?
>
>Michael
>
> >
> > 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
> > spot(array).
> >
> > --Naomi
> >
> >
> > 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.
> > >
> > >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]
> > >
> > >_______________________________________________
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> > >Bioconductor at stat.math.ethz.ch
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> >
> > 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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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