[BioC] present/absent on 2-color oligo arrays
Naomi Altman
naomi at stat.psu.edu
Mon Feb 2 15:57:24 CET 2009
Dear JP,
This is extremely helpful. Since my email below, I did the
analysis. All the foreground spots were higher than the 95th
percentile of the background, and almost all were higher than the
99th percentile. So, I have to confirm your finding, at least qualitatively.
Naomi
At 09:45 AM 2/2/2009, you wrote:
>Hello Naomi,
>I am working working with long-oligo data (70-mer) and our array
>contains negatives and PM/MM sets for selected oligos. In looking at
>intensity of oligos against background it is always higher on the
>spot no matter what is spotted... Even in cases where the
>corresponding gene is known to be not expressed in one tissue.
>
>Negatives and MM with 10 mismatches have much higher intensity than
>their local background. We have discussed it with colleagues. They
>attribute this to two things (maybe both).
>1) The materials spotted on the array produce some fluorescence even
>when nothing hybridized to them
>2) There is (limited) cross-hyb.
>Or a combination of both.
>
>For this reason I am skeptical of using BKG as a criteria to declare
>something expressed, unless it is evident that for non-expressed
>oligos the intensity is in the order of background.
>
>Hope this helps
>JP
>
>
>
>
>
>
>Naomi Altman wrote:
>>Thanks Sam,
>>Unfortunately, we do not have an appropriate set of negative
>>controls on the arrays. We had problems with spike-in controls
>>which we had hoped to use to calibrate
>>the arrays. But it does seem like a good idea. We are going to
>>compare with the background, which is not ideal but gives us some
>>idea of the situation.
>>
>>--Naomi
>>
>>At 08:55 AM 1/31/2009, Samuel Wuest wrote:
>>>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
>>> >
>>> > _______________________________________________
>>> > Bioconductor mailing list
>>> > Bioconductor at stat.math.ethz.ch
>>> > https://stat.ethz.ch/mailman/listinfo/bioconductor
>>> > Search the archives:
>>> > http://news.gmane.org/gmane.science.biology.informatics.conductor
>>> >
>>> >
>>>
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>>
>>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
>>
>>_______________________________________________
>>Bioconductor mailing list
>>Bioconductor at stat.math.ethz.ch
>>https://stat.ethz.ch/mailman/listinfo/bioconductor
>>Search the archives:
>>http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>
>
>--
>=============================
>Juan Pedro Steibel
>
>Assistant Professor
>Statistical Genetics and Genomics
>
>Department of Animal Science & Department of Fisheries and Wildlife
>
>Michigan State University
>1205-I Anthony Hall
>East Lansing, MI
>48824 USA
>Phone: 1-517-353-5102
>E-mail: steibelj at msu.edu
>=============================
>
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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