Dear All, I have done cDNA microarray (dye swap) experiments (two repeats) with samples from wild type and mutant mice(experiment is very similar to Swirl experiment given in tutorials). I tried normalising data with "limma" package with following commands: library(limma) Warning message: package limma was built under R version 1.8.1 RG<-read.maimages(files=c("binita1.txt","binita2.txt","binita3.txt","b inita4.txt"), columns=list(Rf = "CH1_NBC_INT", Rb = "CH1_SPOT_BKGD", Gf = "CH2_NBC_INT", Gb = "CH2_SPOT_BKGD", Name="GENE_DESCRIPTION",verbose=TRUE,sep=" \t", quote = "\"'", dec = ".")) RG$genes<-read.table("binitaSample2.txt", sep="\t",header=TRUE, quote = "\"'",fill=TRUE) layout=list(ngrid.r=2,ngrid.c=12,nspot.r=45,nspot.c=21) RG$printer<-layout RG<-backgroundCorrect(RG, method="minimum") MA<-normalizeWithinArrays(RG,layout=RG$printer) plotMA(MA) As expected, I get MA plot However, when i try normalising the data between the arrays with following commands: MA<-normalizeBetweenArrays(MA) plotMA(MA) The graph flips (if you want i can send graphs) i.e X axis is reversed design<-c(-1,1,-1,1) fit<-lmFit(MA,design) fit<-eBayes(fit) qqt(fit$t,df=fit$df.prior+fit$df.residual,pch=16,cex=0.1) top<-topTable(fit,number=22680,adjust="fdr") ord<-order(fit$lods,decreasing=TRUE) top30<-ord[1:30] plot(A,M,pch=16,cex=0.1) text(A[top30],M[top30],labels=MA$genes[top30,"SPOT.LABEL"],cex=0.8,col ="blue") I get following errors: 1) P.Values which i obtain is 1 or above 1, i tried adjusting P.Value with "Holms" etc but get the same result. However, the same experiment when i analyse thorugh other progams, the P.Values are very less (less than 0.001) for differentially expressed genes. 3) i have problems in subseting topTables also. subset<-subset(topTable,P.Value<0.01,select=MA$genes) Error in subset.default(topTable, P.Value < 0.01, select = MA$genes) : Object "P.Value" not found 2)on MA plot i want to highlight the top 30 genens with "SPOT LABEL" which is there on MA$genes, but the program picks up some number which does not corresonds to the SPOT.LABEL. I have shown here, top 10 genes SPOT.LABEL CIU CLONE.ID M A t P.Value B 4459 4459 MM6 25179 1.91 1.627766 11.18 1 -2.82 14466 14466 MM6 14674 1.62 0.695737 10.35 1 -2.84 17413 17413 MM6 17720 2.48 -1.036538 10.08 1 -2.85 21140 21140 MM6 16316 1.57 0.000431 9.80 1 -2.85 18070 18070 MM6 9920 1.61 1.888210 9.61 1 -2.86 776 776 MM6 27323 1.72 1.873411 9.45 1 -2.86 12014 12014 MM6 25084 1.74 1.226841 9.43 1 -2.87 19423 19423 MM6 20962 -1.44 4.009106 -9.22 1 -2.87 17258 17258 MM6 13529 2.31 0.289187 9.06 1 -2.88 21565 21565 MM6 27496 1.60 1.794250 8.78 1 -2.89 Help in this regards will be highly appreciated. Sincerely yours, Binita ============================== Binita Dutta, PhD MicroArray Facility(MAF) UZ Gasthuisberg Onderwijs en Navorsing Herestraat 49 3000 Leuven Belgium

At 10:19 PM 30/03/2004, Binita Dutta wrote: >Dear All, > >I have done cDNA microarray (dye swap) experiments (two repeats) with >samples from wild type and mutant mice(experiment is very similar to >Swirl experiment given in tutorials). I tried normalising data with >"limma" package with following commands: >library(limma) >Warning message: >package limma was built under R version 1.8.1 >RG<-read.maimages(files=c("binita1.txt","binita2.txt","binita3.txt"," binita4.txt"), >columns=list(Rf = "CH1_NBC_INT", Rb = "CH1_SPOT_BKGD", Gf = "CH2_NBC_INT", >Gb = "CH2_SPOT_BKGD", Name="GENE_DESCRIPTION",verbose=TRUE,sep=" >\t", quote = "\"'", dec = ".")) > RG$genes<-read.table("binitaSample2.txt", sep="\t",header=TRUE, quote = > "\"'",fill=TRUE) >layout=list(ngrid.r=2,ngrid.c=12,nspot.r=45,nspot.c=21) > RG$printer<-layout > RG<-backgroundCorrect(RG, method="minimum") > MA<-normalizeWithinArrays(RG,layout=RG$printer) >plotMA(MA) >As expected, I get MA plot > >However, when i try normalising the data between the arrays with >following commands: >MA<-normalizeBetweenArrays(MA) > plotMA(MA) >The graph flips (if you want i can send graphs) > >i.e X axis is reversed This doesn't make any sense to me, I don't think there is any way that the X axis could reverse. You would have to provide some evidence that something is wrong. >design<-c(-1,1,-1,1) > >fit<-lmFit(MA,design) >fit<-eBayes(fit) > qqt(fit$t,df=fit$df.prior+fit$df.residual,pch=16,cex=0.1) >top<-topTable(fit,number=22680,adjust="fdr") >ord<-order(fit$lods,decreasing=TRUE) >top30<-ord[1:30] >plot(A,M,pch=16,cex=0.1) >text(A[top30],M[top30],labels=MA$genes[top30,"SPOT.LABEL"],cex=0.8,co l="blue") >I get following errors: > >1) P.Values which i obtain is 1 or above 1, i tried adjusting P.Value with >"Holms" etc but get the same result. However, the same experiment when i >analyse thorugh other progams, the P.Values are very less (less than >0.001) for differentially expressed genes. The obvious explanation is that you have assigned treatments incorrectly, e.g., your design matrix is wrong. Are you saying that *all* of your p.values are equal to 1? Are you claiming that you have p.values above 1? As far as I know, that cannot occur. >3) i have problems in subseting topTables also. >subset<-subset(topTable,P.Value<0.01,select=MA$genes) >Error in subset.default(topTable, P.Value < 0.01, select = MA$genes) : > Object "P.Value" not found You are trying to subset a function rather than a data.frame! The *output* from topTable() would be a data.frame. In any case, have you considered using topTable with a smaller 'number' to do what you want? >2)on MA plot i want to highlight the top 30 genens with "SPOT LABEL" which >is there on MA$genes, but the program picks up some number which does not >corresonds to the SPOT.LABEL. This is most likely because your SPOT.LABEL is a factor rather than a character vector. Try setting MA$genes$SPOT.LABEL <- as.character(MA$genes$SPOT.LABEL) Gordon >I have shown here, top 10 genes >SPOT.LABEL CIU CLONE.ID M A t P.Value B >4459 4459 MM6 25179 1.91 1.627766 11.18 1 -2.82 >14466 14466 MM6 14674 1.62 0.695737 10.35 1 -2.84 >17413 17413 MM6 17720 2.48 -1.036538 10.08 1 -2.85 >21140 21140 MM6 16316 1.57 0.000431 9.80 1 -2.85 >18070 18070 MM6 9920 1.61 1.888210 9.61 1 -2.86 >776 776 MM6 27323 1.72 1.873411 9.45 1 -2.86 >12014 12014 MM6 25084 1.74 1.226841 9.43 1 -2.87 >19423 19423 MM6 20962 -1.44 4.009106 -9.22 1 -2.87 >17258 17258 MM6 13529 2.31 0.289187 9.06 1 -2.88 >21565 21565 MM6 27496 1.60 1.794250 8.78 1 -2.89 > >Help in this regards will be highly appreciated. >Sincerely yours, >Binita > >============================== > >Binita Dutta, PhD >MicroArray Facility(MAF) >UZ Gasthuisberg >Onderwijs en Navorsing >Herestraat 49 >3000 Leuven >Belgium > >_______________________________________________ >Bioconductor mailing list >Bioconductor@stat.math.ethz.ch >https://www.stat.math.ethz.ch/mailman/listinfo/bioconductor