Hi, some time ago I have similar problem in Limma. I wanted to remove control spots after normalization. Finally I used this code: i=nt_img_lA$genes$Status=="cDNA" nt_img_lAq=nt_img_lA[i,] Which simply removes all spots with status cDNA. I think you can use something like: i=your_data$weight==0 new_data=your_data[i,] I hope it helps, Regards David martin <vilanew at="" ...=""> writes: > > Ok thanks, > Is there any function witihin limma that would remove the spots ? > > On 04/28/2010 03:41 PM, James W. MacDonald wrote: > > Hi David, > > > > David martin wrote: > >> Hi, > >> I'm have a custom array design with several blocks and each spot in > >> duplicate. I'm running a single channel experiment. Each sample being > >> labeled with the same dye. > >> > >> My problem is that when spots are assigned weight=0 (discarded) they > >> still all appear in the fitted object. I though that assigning a > >> weight of 0 would discard this spots (would be removed from thh > >> analysis). In the documentation this seems to be true for > >> withinarraynormalization SInce this is not the case, how can i remove > >> all these spots ?? > > > > I think you misunderstand the documentation (and the basic idea behind > > weighting data). It never says that data with a weight = 0 are > > discarded. Instead, it says that downstream functions will use these > > weights when analyzing the data. > > > > Since the weights for certain spots are zero, you will effectively > > remove those spots from consideration when normalizing, fitting models, > > etc, but they are not removed from the fitted object. > > > > Best, > > > > Jim > > > > > >> > >> Here is the code: > >> > >> > >> # > >> # Load libraries > >> # > >> library(limma) > >> > >> # This defines the column name of the mean Cy5 foreground intensites > >> Cy5 <- "F635 Mean" > >> > >> # This defines the column name of the mean Cy5 background intensites > >> Cy5b <- "B635 Mean" > >> > >> > >> # Read the targets file (see limmaUsersGuide for info on how to create > >> this) > >> targets <- readTargets("targets.txt") > >> > >> > >> #Read gpr files and weight negative spots as 0 for spots with Flags -50. > >> RG <- read.maimages(targets$FileName, > >> source="genepix", > >> columns=list(R=Cy5,G=Cy5, Rb=Cy5b,Gb=Cy5b), > >> annotation = c("Block", "Column", "Row", "ID", "Name","Flags"), > >> wt.fun=wtflags(weight=0,cutoff=-50), > >> ) > >> > >> # remove the extraneous green channel values > >> RG$G <- NULL > >> RG$Gb <- NULL > >> > >> #Read spotypes and assign controls > >> spottypes<-readSpotTypes("spottypes.txt") > >> RG$genes$Status<-controlStatus(spottypes,RG$genes) > >> > >> #Do background correction > >> bRG <- backgroundCorrect(RG$R,method='normexp') > >> > >> #Normalize > >> MA <- normalizeBetweenArrays(log2(bRG), method="quantile") > >> > >> #Handle duplicates spots > >> corfit <- duplicateCorrelation(MA,ndups=2,spacing=1) > >> > >> fit<-lmFit(MA,correlation=corfit$consensus.correlation,weights=w,ndups =2,genelist=RG$genes$Name) > >> > >> fit<-eBayes(fit) > >> topTable(fit,genelist=RG$genes$Name,number=NULL) > >> > >> _______________________________________________ > >> Bioconductor mailing list > >> Bioconductor at ... > >> https://stat.ethz.ch/mailman/listinfo/bioconductor > >> Search the archives: > >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > _______________________________________________ > Bioconductor mailing list > Bioconductor at ... > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > Maciej Jo?czyk, MSc Department of Plant Molecular Ecophysiology Institute of Plant Experimental Biology Faculty of Biology, University of Warsaw 02-096 Warszawa, Miecznikowa 1 ___________________________________ NOCC, http://nocc.sourceforge.net -- This email was Anti Virus checked by Astaro Security Gateway. http://www.astaro.com

Ok thanks, that would do the trick On 04/29/2010 02:55 PM, Maciej Jo?czyk wrote: > Hi, > > some time ago I have similar problem in Limma. I wanted to remove > control spots after normalization. > > Finally I used this code: > > i=nt_img_lA$genes$Status=="cDNA" > nt_img_lAq=nt_img_lA[i,] > > Which simply removes all spots with status cDNA. > > I think you can use something like: > > i=your_data$weight==0 > new_data=your_data[i,] > > I hope it helps, > > Regards > > David martin<vilanew at="" ...=""> writes: > >> >> Ok thanks, >> Is there any function witihin limma that would remove the spots ? >> >> On 04/28/2010 03:41 PM, James W. MacDonald wrote: >>> Hi David, >>> >>> David martin wrote: >>>> Hi, >>>> I'm have a custom array design with several blocks and each spot in >>>> duplicate. I'm running a single channel experiment. Each sample > being >>>> labeled with the same dye. >>>> >>>> My problem is that when spots are assigned weight=0 (discarded) > they >>>> still all appear in the fitted object. I though that assigning a >>>> weight of 0 would discard this spots (would be removed from thh >>>> analysis). In the documentation this seems to be true for >>>> withinarraynormalization SInce this is not the case, how can i > remove >>>> all these spots ?? >>> >>> I think you misunderstand the documentation (and the basic idea > behind >>> weighting data). It never says that data with a weight = 0 are >>> discarded. Instead, it says that downstream functions will use these >>> weights when analyzing the data. >>> >>> Since the weights for certain spots are zero, you will effectively >>> remove those spots from consideration when normalizing, fitting > models, >>> etc, but they are not removed from the fitted object. >>> >>> Best, >>> >>> Jim >>> >>> >>>> >>>> Here is the code: >>>> >>>> >>>> # >>>> # Load libraries >>>> # >>>> library(limma) >>>> >>>> # This defines the column name of the mean Cy5 foreground > intensites >>>> Cy5<- "F635 Mean" >>>> >>>> # This defines the column name of the mean Cy5 background > intensites >>>> Cy5b<- "B635 Mean" >>>> >>>> >>>> # Read the targets file (see limmaUsersGuide for info on how to > create >>>> this) >>>> targets<- readTargets("targets.txt") >>>> >>>> >>>> #Read gpr files and weight negative spots as 0 for spots with Flags > -50. >>>> RG<- read.maimages(targets$FileName, >>>> source="genepix", >>>> columns=list(R=Cy5,G=Cy5, Rb=Cy5b,Gb=Cy5b), >>>> annotation = c("Block", "Column", "Row", "ID", "Name","Flags"), >>>> wt.fun=wtflags(weight=0,cutoff=-50), >>>> ) >>>> >>>> # remove the extraneous green channel values >>>> RG$G<- NULL >>>> RG$Gb<- NULL >>>> >>>> #Read spotypes and assign controls >>>> spottypes<-readSpotTypes("spottypes.txt") >>>> RG$genes$Status<-controlStatus(spottypes,RG$genes) >>>> >>>> #Do background correction >>>> bRG<- backgroundCorrect(RG$R,method='normexp') >>>> >>>> #Normalize >>>> MA<- normalizeBetweenArrays(log2(bRG), method="quantile") >>>> >>>> #Handle duplicates spots >>>> corfit<- duplicateCorrelation(MA,ndups=2,spacing=1) >>>> >>>> > fit<-lmFit(MA,correlation=corfit$consensus.correlation,weights=w,ndu ps=2,genelist=RG$genes$Name) >>>> >>>> fit<-eBayes(fit) >>>> topTable(fit,genelist=RG$genes$Name,number=NULL) >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at ... >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at ... >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> > > > > > Maciej Jo?czyk, MSc > Department of Plant Molecular Ecophysiology > Institute of Plant Experimental Biology > Faculty of Biology, University of Warsaw > 02-096 Warszawa, Miecznikowa 1 > > > > ___________________________________ > NOCC, http://nocc.sourceforge.net > > > >