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Dick Beyer
★
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@dick-beyer-26
Last seen 7.1 years ago

Has anyone written code to get Affy presence calls?
I use the recommended normalization:
data.eset <- expresso(data, normalize=FALSE, bgcorrect.method="mas",
pmcorrect.method="mas", summary.method="mas")
data.mas <- affy.scalevalue.exprSet(data.eset)
but I am not sure what to do to generate presence calls.
Thanks much,
Dick
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Richard P. Beyer, Ph.D. University of Washington
Tel.:(206) 616 7378 Environmental Health, Box 354695
Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100
Seattle, WA 98105-6099
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*********
On Wed, 15 Jan 2003 bioconductor-request@stat.math.ethz.ch wrote:
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> Today's Topics:
>
> 1. RE: problem with expresso() (Wolfgang Huber)
> 2. Re: problem with expresso() (Laurent Gautier)
> 3. RE: problem with expresso() (Wolfgang Huber)
>
> --__--__--
>
> Message: 1
> From: "Wolfgang Huber" <w.huber@dkfz-heidelberg.de>
> To: "Oliver Hartmann" <hartmann@mailer.uni-marburg.de>,
> "bioconductor" <bioconductor@stat.math.ethz.ch>
> Subject: RE: [BioC] problem with expresso()
> Date: Tue, 14 Jan 2003 13:29:30 +0100
>
> Hi,
>
> Oliver and I discussed this offline last Friday. The reason for the
> confusion seems to be that the summary method "medianpolish" takes
the
> logarithm of the data, while, for example, "avdiff" does not.
However, the
> normalization and data transformation method "vsn" also implies a
data
> transformation that is like the logarithm. Thus, a call like
>
> normalize.AffyBatch.methods <- c(normalize.AffyBatch.methods, "vsn")
> es = expresso(data,
> pmcorrect.method = "pmonly",
> bgcorrect.method = "none",
> normalize.method = "vsn",
> summary.method = "medianpolish")
>
> will effectively take the logarithm of the intensities TWICE. The
same call
> with summary.method = "avdiff" would, however, produce the right
result.
> Not sure how to best resolve this? I could "re-exponentiate" the
data
> returned by "vsn" in normalize.AffyBatch.vsn, such that the
subsequent
> log-transformation done in the summary.method would produce
consistent
> results.
>
> However, here is a question regarding the general architecture of
the affy
> package: where is the right place to take the log-transformation? In
the
> "normalization"? In the "summary.method"? As an extra module? (Since
some
> people, including myself, may argue that log-transformation is not
the only
> thing one can do with microarray data?)
>
> Opinions?
>
> Best regards
> Wolfgang
>
> Division of Molecular Genome Analysis (Poustka Lab)
> German Cancer Research Center (DKFZ)
> Im Neuenheimer Feld 580
> 69120 Heidelberg, Germany
>
> w.huber@dkfz.de
> http://www.dkfz.de/abt0840/whuber
> Tel +49-6221-424709
> Fax +49-6221-42524709
>
>
> -----Original Message-----
> From: bioconductor-admin@stat.math.ethz.ch
> [mailto:bioconductor-admin@stat.math.ethz.ch]On Behalf Of Oliver
> Hartmann
> Sent: Thursday, January 09, 2003 2:47 PM
> To: bioconductor
> Subject: [BioC] problem with expresso()
>
>
> Dear lsit memners,
>
> I am trying to find a way of normalzing affy chips with vsn (I found
a
> data set where rma() doesn't do well together with the t-statistic
and I
> was hopeing that vsn() could fix that). I used the following script:
>
> data <- ReadAffy()
> With this, identifying differentially expressed genes works fine
> (results are very similar to rma() - see my tech report for details
if
> you like).
> But there seems to be one problem: the intensities and the values
\delta
> h for differential expression (equivalent to the difference between
the
> log-ratios if using rma()) are both on the wrong scale. Well, as
rma()
> and other methods use log-transformed data, but vsn() uses a
different
> tranformation, I think using expresso() to calculat vsn-normalized
> measures seems to log- AND arcsin-transform the data. Is there a way
> around that? From the description I didn't find a way around
> log-transformation nor where exactly the log-transformation was
taking
> place.
>
> If you are interested in the comparission of the performance of
rma(),
> vsn() and MAS() tested on affymetrix data with spike in genes you
can
> find a tech report at http://staff-www.uni-marburg.de/~hartmann/ -
but
> only very preliminary work, sorry.
>
> Thanks a lot
>
> -oliver hartmann-
>
> --
> Oliver Hartmann, Institute of Medical Biometry and Epidemiology
> Philipps-University Marburg, Bunsenstr. 3, D-35037 Marburg
> phone +49(0)6421 28 66514, fax +49(0)6421 28 68921
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
>
> --__--__--
>
> Message: 2
> Date: Wed, 15 Jan 2003 01:20:35 +0100
> From: Laurent Gautier <laurent@cbs.dtu.dk>
> To: Wolfgang Huber <w.huber@dkfz-heidelberg.de>
> Cc: Oliver Hartmann <hartmann@mailer.uni-marburg.de>,
> bioconductor <bioconductor@stat.math.ethz.ch>
> Subject: Re: [BioC] problem with expresso()
>
> On Tue, Jan 14, 2003 at 01:29:30PM +0100, Wolfgang Huber wrote:
> > Hi,
> >
> > Oliver and I discussed this offline last Friday. The reason for
the
> > confusion seems to be that the summary method "medianpolish" takes
the
> > logarithm of the data, while, for example, "avdiff" does not.
However, the
> > normalization and data transformation method "vsn" also implies a
data
> > transformation that is like the logarithm. Thus, a call like
> >
> > normalize.AffyBatch.methods <- c(normalize.AffyBatch.methods,
"vsn")
> > es = expresso(data,
> > pmcorrect.method = "pmonly",
> > bgcorrect.method = "none",
> > normalize.method = "vsn",
> > summary.method = "medianpolish")
> >
> > will effectively take the logarithm of the intensities TWICE. The
same call
> > with summary.method = "avdiff" would, however, produce the right
result.
> > Not sure how to best resolve this? I could "re-exponentiate" the
data
> > returned by "vsn" in normalize.AffyBatch.vsn, such that the
subsequent
> > log-transformation done in the summary.method would produce
consistent
> > results.
>
> It would appear to be the right to proceed on my side (see below).
>
> > However, here is a question regarding the general architecture of
the affy
> > package: where is the right place to take the log-transformation?
In the
> > "normalization"? In the "summary.method"? As an extra module?
(Since some
> > people, including myself, may argue that log-transformation is not
the only
> > thing one can do with microarray data?)
>
> This is an interesting question. Some people may even argue for a
transformation
> to be done once the expression values are obtained (i.e. once the
exprSet object is obtained). Here is a suggestion:
> - "intermediate" processing steps must return data on the same scale
than they received them
> - add two paramaters to functions like "normalize", "computeExpr" :
'transfo'
> (and 'untransfo') to specify a transformation to apply before
proceeding (and
> the inverse of the transformation). This would let one toy with
alternatives
> to log transforming... (one might also think about a collection of
'transfo and
> untransfo' included in the package)
>
> Would this appear satisfactory/reasonable ?
>
>
>
> L.
>
>
> >
> > Opinions?
> >
> > Best regards
> > Wolfgang
> >
> > Division of Molecular Genome Analysis (Poustka Lab)
> > German Cancer Research Center (DKFZ)
> > Im Neuenheimer Feld 580
> > 69120 Heidelberg, Germany
> >
> > w.huber@dkfz.de
> > http://www.dkfz.de/abt0840/whuber
> > Tel +49-6221-424709
> > Fax +49-6221-42524709
> >
> >
> > -----Original Message-----
> > From: bioconductor-admin@stat.math.ethz.ch
> > [mailto:bioconductor-admin@stat.math.ethz.ch]On Behalf Of Oliver
> > Hartmann
> > Sent: Thursday, January 09, 2003 2:47 PM
> > To: bioconductor
> > Subject: [BioC] problem with expresso()
> >
> >
> > Dear lsit memners,
> >
> > I am trying to find a way of normalzing affy chips with vsn (I
found a
> > data set where rma() doesn't do well together with the t-statistic
and I
> > was hopeing that vsn() could fix that). I used the following
script:
> >
> > data <- ReadAffy()
> > With this, identifying differentially expressed genes works fine
> > (results are very similar to rma() - see my tech report for
details if
> > you like).
> > But there seems to be one problem: the intensities and the values
\delta
> > h for differential expression (equivalent to the difference
between the
> > log-ratios if using rma()) are both on the wrong scale. Well, as
rma()
> > and other methods use log-transformed data, but vsn() uses a
different
> > tranformation, I think using expresso() to calculat vsn-normalized
> > measures seems to log- AND arcsin-transform the data. Is there a
way
> > around that? From the description I didn't find a way around
> > log-transformation nor where exactly the log-transformation was
taking
> > place.
> >
> > If you are interested in the comparission of the performance of
rma(),
> > vsn() and MAS() tested on affymetrix data with spike in genes you
can
> > find a tech report at http://staff-www.uni-marburg.de/~hartmann/ -
but
> > only very preliminary work, sorry.
> >
> > Thanks a lot
> >
> > -oliver hartmann-
> >
> > --
> > Oliver Hartmann, Institute of Medical Biometry and Epidemiology
> > Philipps-University Marburg, Bunsenstr. 3, D-35037 Marburg
> > phone +49(0)6421 28 66514, fax +49(0)6421 28 68921
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@stat.math.ethz.ch
> > http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
> --
> --------------------------------------------------------------
> currently at the National Yang-Ming University in Taipei, Taiwan
> --------------------------------------------------------------
> Laurent Gautier CBS, Building 208, DTU
> PhD. Student DK-2800 Lyngby,Denmark
> tel: +45 45 25 24 89 http://www.cbs.dtu.dk/laurent
>
>
> --__--__--
>
> Message: 3
> From: "Wolfgang Huber" <w.huber@dkfz-heidelberg.de>
> To: "Laurent Gautier" <laurent@cbs.dtu.dk>
> Cc: "bioconductor" <bioconductor@stat.math.ethz.ch>
> Subject: RE: [BioC] problem with expresso()
> Date: Wed, 15 Jan 2003 11:08:52 +0100
>
> Hi Laurent:
>
> > Here is a suggestion:
> > 1) "intermediate" processing steps must return data on the same
> > scale than they received them
> > 2) add two paramaters to functions like "normalize",
"computeExpr":
> > 'transfo' (and 'untransfo') to specify a transformation to apply
before
> > Would this appear satisfactory/reasonable ?
>
> The combinatorics of all those different method could become quite
> overwhelming. And that means also: potentially prone to bugs or user
> mistakes, and inefficient (computation time, memory). To be able to
combine
> the different methods freely is extremely useful for people working
on
> method comparisons, but is this really the main goal of the affy
package?
>
> I still do not fully understand why there are both express() and
expresso()
> methods, and in addition there is now also a standalone
implementation of
> RMA in C. But could it be that this reflects the limitations of the
> combinatorial approach?
>
> Another approach that I'd suggest is to expect people that want to
plug
> together all sorts of different background adjustment,
normalization,
> transformation and probeset-summary methods to do so on their own
> responsibility.
>
> And for everyone else, you (we) can offer a small number of
functions like
> rma(), express(o) with limited options, that we have found to make
sense.
>
> What do you think?
>
> Best regards
> Wolfgang
>
>
>
> --__--__--
>
> _______________________________________________
> Bioconductor mailing list
> Bioconductor@stat.math.ethz.ch
> http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
>
>
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