Hi Jenny Thank you very much for your help. It works now. These analyses are so interesting I'm still learning :-). Regards, Sherosha 2009/1/13 Jenny Drnevich <drnevich at="" illinois.edu="">: > Hi Sherosha, > > The description of genefilter() says: > > genefilter filters genes in the array expr using the filter functions in > flist. It returns an array of logical values (suitable for subscripting) of > the same length as there are rows in expr. For each row of expr the returned > value is TRUE if the row passed all the filter functions. Otherwise it is > set to FALSE. > > Your output object "selected" is just a vector of TRUEs and FALSEs, so I'm > assuming you used it this way if you were going to filter BEFORE the > statistical analysis: > >> selected=genefilter(all.esetsub[,61:102],ff) > >> fit=lmFit(all.esetsub[selected,61:102],design) > > If you want use the same filters, which are selecting genes based on the > normalized data, but not filter the genes out until after the analysis, you > would do: > >> selected=genefilter(all.esetsub[,61:102],ff) #same as above > >> fit=lmFit(all.esetsub[,61:102],design) > >> fit.filtered <- fit[selected,] > > HTH, > Jenny > > > At 12:09 PM 1/13/2009, Sherosha Raj wrote: >> >> Hello Jenny >> >> This is how I setup the filters: >> >> #setup filters >> > f1=pOverA(0.25,log2(100)) >> > f2=function(x)(IQR(x)>0.5) >> > ff=filterfun(f1,f2) >> >> Around here I sub-select the probesets that come through the filter >> from my expression set. >> then proceed to >> >> #LIMMA >> >> >targets=readTargets("targets.txt",sep="") >> > WD=paste(targets$.....) >> > WD=factor(WD,levels=c(".........")) >> > design=model.matrix(~0+WD) >> > colnames(design)=levels(WD) >> > fit=lmFit(all.esetsub[,61:102],design) #from a large eset normalised >> > over 102 chips so subsetting the relevant cel files >> >> #Contrast matrix >> > contmatrix=makeContrasts(.........,levels=design) >> >> >fit2=contrasts.fit(fit,contmatrix) >> >> If I were to filter here using the two filters above...... >> >> >selected=genefilter(fit2,ff) >> > sum(selected) >> [1] 0 >> > class(fit2q) >> [1] "MArrayLM" >> attr(,"package") >> [1] "limma" >> >> #When I filter before starting limma, I get 11504 probesets coming >> through. >> #I am confused how to proceed with the next steps....(i.e subset the >> fit2 object and apply the eBayes)..:-( >> >> #previously proceeded as follows after the "contrasts.fit" step: >> >fit2=eBayes(fit2) >> > changinggenes.05=decideTests(fit2,adjust.method="BH",p.value=0.05) >> >> etc etc >> >> >> I have been previously using filters before limma, but I 've been >> following the discussions on this board and would try to see how the >> data looks if I filtered prior o the eBayes step. >> >> >> Any help is greatly appreciated!! >> Thank you very much! >> Regards, >> Sherosha >> >> 2009/1/13 Jenny Drnevich <drnevich at="" illinois.edu="">: >> > Hi Sherosha, >> > >> > In general, you can filter by subsetting a MArrayLM object the exact >> > same >> > way as you would an ExpressionSet object. If you have any trouble, >> > please >> > post the code that you are trying to use. >> > >> > Cheers, >> > Jenny >> > >> > At 10:47 AM 1/13/2009, Sherosha Raj wrote: >> >> >> >> Hello all >> >> >> >> I"m sorry if this is a simple question, but how does one go about >> >> filtering after the eBayes step since the resulting object is of the >> >> class MArrayLM? >> >> I am used to filtering expression sets directly. >> >> >> >> Thank you very much! >> >> Sherosha >> >> > >> >> > ---------- Forwarded message ---------- >> >> > From: "James W. MacDonald" <jmacdon at="" med.umich.edu=""> >> >> > To: Daniel Brewer <daniel.brewer at="" icr.ac.uk=""> >> >> > Date: Mon, 12 Jan 2009 09:25:02 -0500 >> >> > Subject: Re: [BioC] Filtering before differential expression analysis >> >> > of >> >> > microarrays - New paper out >> >> > Hi Dan, >> >> > >> >> > Daniel Brewer wrote: >> >> >> >> >> >> Hi, >> >> >> >> >> >> There is a new paper out at BMC bioinformatics that seems to justify >> >> >> the >> >> >> use of filtering before differential expression analysis is >> >> >> performed >> >> >> (Hackstadt & Hess BMC Bioinformatics 2009, 10:11 - >> >> >> http://www.biomedcentral.com/1471-2105/10/11/abstract). >> >> >> Specifically >> >> >> filtering by variance and detection call. I have got the impression >> >> >> from this list that the general opinion is that one should only >> >> >> filter >> >> >> out the control genes before testing. I was wondering if anyone had >> >> >> any >> >> >> opinions on this paper and the topic in general. >> >> > >> >> > I'm sure people do have opinions about this topic ;-D >> >> > >> >> > The reason people have so many opinions is because it isn't a simple >> >> > question, and it depends on what you consider important. >> >> > >> >> > If you are just trying to limit the number of multiple comparisons to >> >> > increase power, then filtering first is probably the way to go. >> >> > >> >> > If you are concerned with the accuracy of the FDR estimates, then >> >> > filtering first may not be ideal. >> >> > >> >> > If you are using limma (Hackstadt and Hess used multtest), then you >> >> > should filter after the eBayes step but before the FDR step, as an >> >> > assumption of the eBayes step is that all of the data from the chip >> >> > are >> >> > available. >> >> > >> >> > Unless of course you are concerned about the accuracy of the FDR >> >> > estimates, in which case... well you see the point. >> >> > >> >> > With microarray data analysis the arguments for and against a >> >> > particular >> >> > way of doing things can shed more heat than light, as nobody really >> >> > knows >> >> > the underlying truth, and the measures we use are really far removed >> >> > from >> >> > the actual phenomenon we are testing. >> >> > >> >> > Best, >> >> > >> >> > Jim >> >> > >> >> > >> >> >> >> >> >> Many thanks >> >> >> >> >> >> Dan >> >> >> >> >> > >> >> > -- >> >> > James W. MacDonald, M.S. >> >> > Biostatistician >> >> > Hildebrandt Lab >> >> > 8220D MSRB III >> >> > 1150 W. Medical Center Drive >> >> > Ann Arbor MI 48109-5646 >> >> > 734-936-8662 >> >> > >> >> > >> >> > >> >> >> >> _______________________________________________ >> >> 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 illinois.edu >> > > > -- Regards, Sherosha