On Dec 7, 2005, at 3:17 PM, Tim Smith wrote: > Thanks for the replies Sean and Naomi. I feel I'm on the right track. > > I did the following: > > > sam.out <- sam(zz, ncl, method = "d.stat", delta = NULL, > n.delta = 10, p0 = NA, > lambda = seq(0, 0.95, 0.05), ncs.value = "max", ncs.weights = NULL, > gene.names = dimnames(zz)[[1]]) > > > sam.sum <-summary(sam.out,2,ll=FALSE) > > > Now, I wanted to get to the d-value matrix. The documentation > says that I can get this by the line: > > # The matrix containing the d-values, q-values etc. of the > # differentially expressed genes can be obtained by > > > sam.outATmat.sig # sam.out at mat.sig > > but when I do this, I get : > > Error: no slot of name "mat.sig" for this object of class "SAM" > > How can I access the matrix to get at the underlying d-values > etc..?? > > many thanks. Without going into too much detail, in R you have S3 objects and S4 objects. You use $ for S3 objects and @ for S4 objects. Valid names may be found by names(object) for S3 objects and slotNames(object) for S4 objects. Kasper > Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > > > > On 12/7/05 12:49 PM, "Tim Smith" wrote: > >> Hi all, >> >> A newbie to Bioconductor, so please forgive the naive question.. >> >> I had two sets of data (normalized affy data). The first set contains >> cancerous expression data for 40 patients, and the second set >> contains >> expression data for 30 'normal' (i.e non cancerous) patients. >> >> I wanted to find out the genes that are 'differentially expressed' >> in the >> two sets. Which package, and which functions can I use? Any >> reasonably popular >> method/package would work for me. > > Welcome to bioconductor. > > See here for a full list. Best to do a little reading, as each > method has > potentially different assumptions, etc. > > http://www.bioconductor.org/packages/bioc/1.7/src/contrib/html/ > > That said, look at siggenes which contains the SAM method; the > multtest > package for traditional t-tests; and the limma package. > > And just to be sure, when you say you have two sets of normalized > data, you > mean that they were normalized together or separately? > > Sean > > > > > > > --------------------------------- > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor

I have not used this particular Bioconductor package. But I have learned in general how to deal with the various types of objects that the Bioconductor developers have created for us. class(myobject) tells you the class of "myobject" ?myclass should give the documentation of what the class "myclass" consists of method(myobject) tells you what methods (and slots) work with "myobject" names(myobject) tells you what the components of "myobject" are. Using these functions and the documentation, (vignettes and html help) I can usually figure out what I need. If not ... there is always the mailing list. --Naomi At 06:17 PM 12/7/2005, Tim Smith wrote: >Thanks for the replies Sean and Naomi. I feel I'm on the right track. > > I did the following: > > > sam.out <- sam(zz, ncl, method = "d.stat", delta = NULL, > n.delta = 10, p0 = NA, > lambda = seq(0, 0.95, 0.05), ncs.value = "max", ncs.weights = NULL, > gene.names = dimnames(zz)[[1]]) > > > sam.sum <-summary(sam.out,2,ll=FALSE) > > > Now, I wanted to get to the d-value matrix. The documentation > says that I can get this by the line: > > # The matrix containing the d-values, q-values etc. of the > # differentially expressed genes can be obtained by > > > sam.outATmat.sig # sam.out at mat.sig > > but when I do this, I get : > > Error: no slot of name "mat.sig" for this object of class "SAM" > > How can I access the matrix to get at the underlying d-values etc..?? > > many thanks. > >Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > > > >On 12/7/05 12:49 PM, "Tim Smith" wrote: > > > Hi all, > > > > A newbie to Bioconductor, so please forgive the naive question.. > > > > I had two sets of data (normalized affy data). The first set contains > > cancerous expression data for 40 patients, and the second set contains > > expression data for 30 'normal' (i.e non cancerous) patients. > > > > I wanted to find out the genes that are 'differentially expressed' in the > > two sets. Which package, and which functions can I use? Any > reasonably popular > > method/package would work for me. > >Welcome to bioconductor. > >See here for a full list. Best to do a little reading, as each method has >potentially different assumptions, etc. > >http://www.bioconductor.org/packages/bioc/1.7/src/contrib/html/ > >That said, look at siggenes which contains the SAM method; the multtest >package for traditional t-tests; and the limma package. > >And just to be sure, when you say you have two sets of normalized data, you >mean that they were normalized together or separately? > >Sean > > > > > > >--------------------------------- > > > [[alternative HTML version deleted]] > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor 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