[BioC] 2 color data...

[Jenny Drnevich]

Hi Miles,

We are all trying to say: Why do you think a channel with a near- or 
below-background is a "bad" value and needs to be removed? If the gene is 
not expressed in one treatment, then it should have a 'zero' value, which 
due to array technology will be some positive number near background 
fluorescence. By removing the value completely, you are saying in the 
analysis "there is no information for that sample on that array", but you 
do have information on that sample - it was below detectable level. Even 
though the number that comes out of the background correction, either 0.5 
or 1 as suggested, is not entirely accurate, it is relatively accurate to 
numbers a good deal higher. Conversely, you should not throw away saturated 
values either, because even though you don't know exactly how large they 
were, you do know they were large. If both channels of a spot are 
near/below background on every single array in your experiment, then you 
can remove the entire gene/spot from the analysis.

Cheers,
Jenny



At 09:10 AM 7/19/2006, Naomi Altman wrote:
>I would not do this.  Use less background correction, (e.g. don't
>background correct, or subtract 1/2 of the background), or set the
>channels that are below background to some low value (e.g. 1) so that
>logs can be used.
>
>--Naomi
>
>At 09:48 AM 7/19/2006, you wrote:
> >Thanks for your quick response. I will not delete the gene completely 
> (if you
> >delete genes then LIMMA doesn't know how to handle genes lists with 
> different
> >orders), but although it is helpful to keep genes that may have
> >information in
> >one array, I do think it may be necessary to "NA" the below background 
> values
> >and keep the above background ones. Thus you still have the good values but
> >have eliminated possible bad ones. What do you think of this?
> >-greg
> >
> >Quoting Naomi Altman <naomi at stat.psu.edu>:
> >
> > > I would not delete data that is below background, even in both
> > > channels, if it is above background on at least one array.
> > >
> > > It seems to me that it is important information to know that a gene
> > >
> > > does not express under some condition in your experiment.  Of course,
> > >
> > > the unfortunate side-effect of our liking to use ratios is that
> > > "zero" is not handled well.  But a gene that expresses in some
> > > conditions of interest but not in others surely is of primary
> > > interest to your study.
> > >
> > > --Naomi
> > >
> > > At 11:48 AM 7/18/2006, milesg at bu.edu wrote:
> > > >HI, my name is Gregory Miles. I'm at Boston University and was given
> > > this
> > > >address by Dr. Carey (I went to a seminar of his last week) at the
> > > Harvard
> > > >medical school and was told that I could ask my question about 2
> > > >color data to
> > > >you. On the mouse microarray dataset we have, there are two colors,
> > > and
> > > >therefore two values that can be below background. When both values
> > > are above
> > > >background (zero_barcode on our chip), we keep the data and when
> > > both are
> > > >below we eliminate the data (they become NA). I imagine this is a
> > > correct
> > > >approach, but what should be done regarding the data that has one
> > > intensity
> > > >below background and one above. Would it be best to keep the good
> > > >value? Do we
> > > >eliminate the entire gene from entry into bioconductor? Perhaps
> > > >there is a way
> > > >to specify to bioconductor that this is the case (by entering a
> > > background
> > > >value) and allowing it to handle the data abstractly? Or is it best
> > > to let
> > > >Bioconductor look at them as NA's. Any help would be greatly
> > > appreciated.
> > > >Thanks!
> > > >-Greg Miles
> > > >
> > > >_______________________________________________
> > > >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
> > >
> > > 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
> > >
> > >
>
>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

Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
USA

ph: 217-244-7355
fax: 217-265-5066
e-mail: drnevich at uiuc.edu