[BioC] present/absent on 2-color oligo arrays

Samuel Wuest wuests at tcd.ie
Sat Jan 31 14:55:38 CET 2009

Hi Naomi,

I am doing work on AffyChips, but my samples are from amplified
material with inputs from around 500pico - few nanogram range; I found
that the Affy-Algorithmus "MAS5 calls" was not very happy with this
type of data and thus compared expression values on the chip with an
empirical negative-distribution (background-distribution), please
refer to people.brandeis.edu/~dtaylor/Taylor_Papers/BIBE07_PANP.pdf
for details (or the Bioconductor-package panp)... I worked on the
Arabidopsis chip, and there are "negative" probes on the chip: probes
that do not match DNA sequences from newer genome releases anymore...
I found that by combining the panp-strategy and the information on
negative probes on the chip, I could generate precise and relatively
accurate predictions on the expression state of a gene (I can't give
you all the details so far).

So if there are negative controls/negative probes on the array, you
could use them to generate an empirical background-distribution for
each array and then compare your other signals to this.... Depends on
how many negative probes you'd have... The method works well with the
Affy HGU133-series, please refer to the above mentioned sources...

Hope this helps??

Best, Sam

2009/1/30 Naomi Altman <naomi at stat.psu.edu>:
> I have been having an on-going discussion with a colleague about whether he
> can say that some genes are "absent" in some tissues based on two-color
> microarrays - most recently, Agilent arrays.  There are a number of reasons
> that he would like to do this which are a mix of biology and QC.
> He wants to use some (arbitrary) normalized expression level, or
> unnormalized level above local background or a percentile of the whole array
> background or ...
> Any suggestions for papers about this?  (We can both think of a dozen ways
> to do it, but without experiments to see if they are valid methods, or at
> least a paper to
> cite, I am reluctant to put the statistical seal of approval on any of
> them.)
> Thanks,  Naomi
> p.s. In case anyone thinks that high-throughput sequencing is going to end
> this type of discussion, have a look at the interesting paper by 't Hoen
> comparing sequencing and microarray results.
> http://www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed&cmd=Retrieve&list_uids=18927111
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
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