mas 5.0 presence calls
1
0
Entering edit mode
Dick Beyer ★ 1.4k
@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 ********************************************************************** ********* 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 ********************************************************************** ********* On Wed, 15 Jan 2003 bioconductor-request@stat.math.ethz.ch wrote: > Send Bioconductor mailing list submissions to > bioconductor@stat.math.ethz.ch > > To subscribe or unsubscribe via the World Wide Web, visit > http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor > or, via email, send a message with subject or body 'help' to > bioconductor-request@stat.math.ethz.ch > > You can reach the person managing the list at > bioconductor-admin@stat.math.ethz.ch > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Bioconductor digest..." > > > 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 > > > End of Bioconductor Digest >
Microarray Normalization Cancer affy vsn Microarray Normalization Cancer affy vsn • 796 views
ADD COMMENT
0
Entering edit mode
@hinnerk-boriss-111
Last seen 7.1 years ago
Hi there, Can anyone tell me how to use the phenoData item. For one thing in a technical sense of how to populate the object with data. And also in the analysis sense, of how to use the data once they are there. In other words, is it possible use those data as factors in ANOVA or plots etc? Thanks for your help! Best, Hinnerk
ADD COMMENT
0
Entering edit mode
Hi Hinnerk, It is simply a dataframe with as many rows (observations) as there are columns (chips/arrays) in the exprs matrix. You can populate it with data as you would populate any dataframe, and use the data in models and plots just as with any dataframe. 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 Hinnerk Boriss Sent: Thursday, January 16, 2003 10:02 AM To: 'Bioconductor' Subject: [BioC] phenodata Hi there, Can anyone tell me how to use the phenoData item. For one thing in a technical sense of how to populate the object with data. And also in the analysis sense, of how to use the data once they are there. In other words, is it possible use those data as factors in ANOVA or plots etc? Thanks for your help! Best, Hinnerk _______________________________________________ Bioconductor mailing list Bioconductor@stat.math.ethz.ch http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor
ADD REPLY
0
Entering edit mode
Hi there! I am still having difficulties setting up the phenodata properly. I did set up my data.frame like Wolfgang suggested to do it: chip.group <- rep(seq(1:4),each=3) chip.group2 <- c("a","a","a","b","b","b","c","c","c","d","d","d") contrasts1 <- c("T","T","T","F","F","F","T","T","T","F","F","F") contrasts2 <- c("F","F","F","F","F","F","T","T","T","T","T","T") chip.names=c("nC1","nC2","nC3","1h1","1h2","1h3","4h1","4h2","4h3","C1 ", "C2","C3") annotation <- data.frame(chip.group,chip.group2,chip.names,contrasts1,contrasts2) pD <- new("phenoData", pData=annotation) ExData@phenoData <- pD This works fine, except that the varLabels aren't set: > ExData.rma Expression Set (exprSet) with 12625 genes 12 samples phenoData object with 5 variables and 12 cases varLabels > and the covariate isn't used properly: table(ExData.rma[["chip.group2"]]) a b c d 0 0 0 0 What is it that I am still doing wrong here? Best, Hinnerk > -----Original Message----- > From: bioconductor-admin@stat.math.ethz.ch > [mailto:bioconductor-admin@stat.math.ethz.ch] On Behalf Of > Wolfgang Huber > Sent: Thursday, January 16, 2003 10:50 AM > To: Hinnerk Boriss; 'Bioconductor' > Subject: RE: [BioC] phenodata - rtfm > > > Hi Hinnerk, > > It is simply a dataframe with as many rows (observations) as there are > columns (chips/arrays) in the exprs matrix. You can populate > it with data as > you would populate any dataframe, and use the data in models > and plots just > as with any dataframe. > > 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 > Hinnerk Boriss > Sent: Thursday, January 16, 2003 10:02 AM > To: 'Bioconductor' > Subject: [BioC] phenodata > > > Hi there, > > Can anyone tell me how to use the phenoData item. For one thing in a > technical sense of how to populate the object with data. And > also in the > analysis sense, of how to use the data once they are there. In other > words, is it possible use those data as factors in ANOVA or plots etc? > > Thanks for your help! > > Best, > Hinnerk > > _______________________________________________ > 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 >
ADD REPLY
0
Entering edit mode
the Biobase function read.phenoData can help you enter phenodata. you can either read a file or input them through a widget. hope this helps, -rafael On Thu, 16 Jan 2003, Hinnerk Boriss wrote: > Hi there, > > Can anyone tell me how to use the phenoData item. For one thing in a > technical sense of how to populate the object with data. And also in the > analysis sense, of how to use the data once they are there. In other > words, is it possible use those data as factors in ANOVA or plots etc? > > Thanks for your help! > > Best, > Hinnerk > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > http://www.stat.math.ethz.ch/mailman/listinfo/bioconductor >
ADD REPLY

Login before adding your answer.

Traffic: 293 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6