Dear Naomi, Gordon and Ben,



Thank you for your replies to Ben Tupper’s (and my) question.



We are using spotted oligonucleotide microarrays containing probes for both
host and virus genes. In our experiment we had cultures grown under high
and low phosphate conditions, inoculated with 2 different viruses
(separately) or kept virus-free, in triplicate. RNA purified from those
cultures at different time points was fluorescently labeled (with Cy-dyes)
and hybridized onto the microarray slides. You can see a flow chart of our
experimental design here:

http://dl.dropbox.com/u/8433654/design-concept.pdf



One slide contains 2 samples which had different experimental treatments.
Each sample was split into 3, labeled (dye swap) and hybridized onto 3
different microarray slides in combination with another sample to allow
technical replication.



I quantified labeling efficiency prior to hybridizing the samples onto the
microarray slide, for both dyes I got between 30 and 60 dye molecules per
1000 nt (what is the range indicated by the manufacturer for good
labeling). Also we produced FB plots for the green and the red channels,
both had similar z-range and saturation range, which we interpreted as a
proof of good labeling (?). See example:

http://dl.dropbox.com/u/8433654/R-G-imageplot.png



Both MA clusters that we observe contain a mixture of both host and virus
probes, ruling out that one complete set of probes failed. Naomi mentioned
that the nondifferentially expressing genes should cluster around M=0, so
does that mean that the top cluster corresponds to differentially expressed
genes?



We used GenePix Pro to scan and analyze the microarrays. Could we use the
normalization function in the software (normalize the data in each image so
that the mean of the median of ratios of all features is equal to 1) as an
alternative to MA? Or would that simply hide the problem? And then do
normalization between arrays using the quantile method?


Thanks,

Joaquin



> > From: Naomi Altman <naomi@stat.psu.edu>
> > Date: January 18, 2012 9:56:45 AM EST
> > To: Gordon K Smyth <smyth@wehi.EDU.AU>, Ben Tupper <btupper@bigelow.org>
> > Cc: Bioconductor mailing list <bioconductor@r-project.org>
> > Subject: Re: [BioC] Two populations on microarray
> >
> > Dear Ben,
> > A typical MA plot has most of the points scattered around the line M=0.
>  Even if you have 2 populations of probes, the nondifferentially expressing
> genes should be in that central ellipse.  (The lower cluster does look
> somewhat like the typical MA plot for raw data.)  I suggest that you do
> separate MA plots for each population of probes, to see if one set of
> probes failed.  Or, as Gordon suggests, a population for which labelling
> failed.
> >
> > --Naomi
> >
> >
> > At 05:48 PM 1/14/2012, Gordon K Smyth wrote:
> >> Dear Ben,
> >>
> >> Are you saying that you have deliberately designed two different
> populations of probes onto your arrays?
> >>
> >> Your MA-plot suggests that there is substantial body of spots on the
> array for which the green channel has failed, hence the 45-degree line at
> the top of the plot.  These dots likely represent spots with a normal red
> channel value but close to zero for green.  Normally this would have a
> technical rather than biological cause.  An imageplot may help you identify
> where the offending spots are on your array.
> >>
> >> On the other hand, if you have deliberately spotted your arrays with
> two quite different populations of probes, then they probably need to be
> analysed as separate arrays.
> >>
> >> Best wishes
> >> Gordon
> >>
> >>> Date: Thu, 12 Jan 2012 14:28:36 -0500
> >>> From: Ben Tupper <btupper@bigelow.org>
> >>> To: bioconductor@r-project.org
> >>> Subject: [BioC] Two populations on microarray
> >>>
> >>> Hello,
> >>>
> >>> By virtue of experiment design we have two populations to analyze on
> each of a suite of Genepix microarrays.  You can see an example in an MA
> plot here (generated using the excellent limma package) :
> >>>
> >>>        http://dl.dropbox.com/u/8433654/BE%20T46h%20slide%2052.png
> >>>
> >>> We have been following the steps in the limma user guide, and Ben
> Bolstad's helpful notes http://tinyurl.com/7346mh9 All of the examples we
> see appear to have just one population to contend with, which gives us an
> inkling that we are being naive about our analysis.  We suspect that we'll
> have to separate the two populations before normalization and analysis.
>  Are there any guides available for managing two populations like this?
> >>>
> >>> Thanks!
> >>> Ben
> >>>
> >>>
>
>

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