Hi: I performed a microarray (5 biological replicates) and analyzed using Limma. The software gives me the M value as a combination from the 5 biological replicates microarray. In order to submit the data to GEO database we need the M-value for each array ( 5 M-values). How can I do that? I'll appreciate your help. Below there is a copy of the sequence for the analysis: library(limma) library(geneplotter) ###Lee targets y image files targets=readTargets("maTargetFKS12.txt") RG=read.maimages(files=cbind(targets$FileNameCy3,targets$FileNameCy5), source="imagene") ##lear gal gal=readGAL("013384_D_20050601.gal") gal=gal[1:nrow(RG$R),] ##asignar el gal RG$genes=gal ###definir los Spot Types y colores spottypes <- readSpotTypes() ##usamos el default que es SpotTypes RG$genes$Status <- controlStatus(spottypes, RG) ###definir el layour printer= list(ngrid.r=1,ngrid.c=1,nspot.r=107,nspot.c=101) RG$printer=structure(printer, class = "PrintLayout") ###normalize: background y normlize within RGoriginal = RG RG <- backgroundCorrect(RGoriginal, method="normexp", offset=50) MA = normalizeWithinArrays(RG,method="loess",bc.method="none") ##escoger los genes nadamas geneIndex = which(MA$genes$Status=="gene") ##preparar el disenyo para fit design = modelMatrix(targets,ref="WT") ###ver todos los MA plots como QC: todos OK! for(i in 1:6){ plotMA(MA[geneIndex,i],main=targets$Name[i],ylim=range(MA$M[geneInde x,])) scan() } ###estimar los paramteros usando eBayes (moderated t-stat) y calcular FDR fit1 = lmFit(MA,design) fit1 = eBayes(fit1) tt=topTable(fit1,adjust="fdr",number=3000) tt=tt[tt$P.Value<=0.01,] ###Escoger genes para incluir en las graficas Index=as.numeric(rownames(tt)) interestGenes <- c("CHS3","SLT2","PIR3","ECM4","ECM13","SPI1","ORF:YHR097C","MYO1","PIR 3","HOG1","SWE1") interestIndex <- lapply(interestGenes,function(x) which(MA$genes$GeneName==x)) names(interestIndex) <- interestGenes ##MA-PLOT plot(fit1$Amean[geneIndex],fit1$coef[geneIndex],pch=".",col="grey",yla b="M",xlab="A") points(fit1$Amean[Index],fit1$coef[Index],pch=16,col="blue",cex=.5) for(i in seq(along=interestGenes)){ text(fit1$Amean[interestIndex[[i]]],fit1$coef[interestIndex[[i]]],inte restGenes[i],cex=.5) } ##VOLCANO plot(fit1$coef[geneIndex],-log10(fit1$p.value[geneIndex]),pch=".",col= "grey",xlab="M",ylab="-log_10 p-value") points(fit1$coef[Index],-log10(fit1$p.value[Index]),pch=16,col="blue", cex=.5) for(i in seq(along=interestGenes)){ text(fit1$coef[interestIndex[[i]]],-log10(fit1$p.value[interestIndex[[ i]]]),interestGenes[i],cex=.5) } ###hacer xls file todos = topTable(fit1,adjust="fdr",number=10807) write.csv(todos,file="myo1.csv") -- José F. Rodríguez Quiñones Graduate student School of Medicine Medical Sciences Campus University of Puerto Rico [[alternative HTML version deleted]]

On Tue, Sep 30, 2008 at 9:42 AM, Jose Francisco Rodriguez <francquimico at="" gmail.com=""> wrote: > Hi: > I performed a microarray (5 biological replicates) and analyzed using > Limma. The software gives me the M value as a combination from the 5 > biological replicates microarray. In order to submit the data to GEO > database we need the M-value for each array ( 5 M-values). How can I do > that? I'll appreciate your help. MA$M contains the M values for each array. Sean > Below there is a copy of the sequence for the analysis: > > library(limma) > library(geneplotter) > > ###Lee targets y image files > targets=readTargets("maTargetFKS12.txt") > RG=read.maimages(files=cbind(targets$FileNameCy3,targets$FileNameCy5), > source="imagene") > > ##lear gal > gal=readGAL("013384_D_20050601.gal") > gal=gal[1:nrow(RG$R),] > ##asignar el gal > RG$genes=gal > > ###definir los Spot Types y colores > spottypes <- readSpotTypes() ##usamos el default que es SpotTypes > RG$genes$Status <- controlStatus(spottypes, RG) > > ###definir el layour > printer= list(ngrid.r=1,ngrid.c=1,nspot.r=107,nspot.c=101) > RG$printer=structure(printer, class = "PrintLayout") > > ###normalize: background y normlize within > RGoriginal = RG > RG <- backgroundCorrect(RGoriginal, method="normexp", offset=50) > MA = normalizeWithinArrays(RG,method="loess",bc.method="none") > > ##escoger los genes nadamas > geneIndex = which(MA$genes$Status=="gene") > > ##preparar el disenyo para fit > design = modelMatrix(targets,ref="WT") > > ###ver todos los MA plots como QC: todos OK! > for(i in 1:6){ > plotMA(MA[geneIndex,i],main=targets$Name[i],ylim=range(MA$M[geneInd ex,])) > scan() > } > > ###estimar los paramteros usando eBayes (moderated t-stat) y calcular FDR > fit1 = lmFit(MA,design) > fit1 = eBayes(fit1) > tt=topTable(fit1,adjust="fdr",number=3000) > tt=tt[tt$P.Value<=0.01,] > > ###Escoger genes para incluir en las graficas > Index=as.numeric(rownames(tt)) > interestGenes <- > c("CHS3","SLT2","PIR3","ECM4","ECM13","SPI1","ORF:YHR097C","MYO1","P IR3","HOG1","SWE1") > interestIndex <- lapply(interestGenes,function(x) > which(MA$genes$GeneName==x)) > names(interestIndex) <- interestGenes > > ##MA-PLOT > plot(fit1$Amean[geneIndex],fit1$coef[geneIndex],pch=".",col="grey",y lab="M",xlab="A") > points(fit1$Amean[Index],fit1$coef[Index],pch=16,col="blue",cex=.5) > for(i in seq(along=interestGenes)){ > > text(fit1$Amean[interestIndex[[i]]],fit1$coef[interestIndex[[i]]],in terestGenes[i],cex=.5) > } > > ##VOLCANO > plot(fit1$coef[geneIndex],-log10(fit1$p.value[geneIndex]),pch=".",co l="grey",xlab="M",ylab="-log_10 > p-value") > points(fit1$coef[Index],-log10(fit1$p.value[Index]),pch=16,col="blue ",cex=.5) > for(i in seq(along=interestGenes)){ > > text(fit1$coef[interestIndex[[i]]],-log10(fit1$p.value[interestIndex [[i]]]),interestGenes[i],cex=.5) > } > > > ###hacer xls file > todos = topTable(fit1,adjust="fdr",number=10807) > write.csv(todos,file="myo1.csv") > -- > Jos? F. Rodr?guez Qui?ones > Graduate student > School of Medicine > Medical Sciences Campus > University of Puerto Rico > > [[alternative HTML version deleted]] > > > _______________________________________________ > 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 >

On Tue, Sep 30, 2008 at 10:47 AM, Jose Francisco Rodriguez <francquimico at="" gmail.com=""> wrote: > Hi: > Thank You! I applied this command (MA$M) and obtained the values, but I'm > having problems creating an excel table for those. I suppose that I should > use write.table, but I dont know how to put the command without giving me an > error. Sorry but I'm still learning. What have you tried? And what error do you get? Sean > On 9/30/08, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: >> >> On Tue, Sep 30, 2008 at 9:42 AM, Jose Francisco Rodriguez >> <francquimico at="" gmail.com=""> wrote: >> > Hi: >> > I performed a microarray (5 biological replicates) and analyzed using >> > Limma. The software gives me the M value as a combination from the 5 >> > biological replicates microarray. In order to submit the data to GEO >> > database we need the M-value for each array ( 5 M-values). How can I do >> > that? I'll appreciate your help. >> >> MA$M contains the M values for each array. >> >> Sean >> >> > Below there is a copy of the sequence for the analysis: >> > >> > library(limma) >> > library(geneplotter) >> > >> > ###Lee targets y image files >> > targets=readTargets("maTargetFKS12.txt") >> > RG=read.maimages(files=cbind(targets$FileNameCy3,targets$FileNameCy5), >> > source="imagene") >> > >> > ##lear gal >> > gal=readGAL("013384_D_20050601.gal") >> > gal=gal[1:nrow(RG$R),] >> > ##asignar el gal >> > RG$genes=gal >> > >> > ###definir los Spot Types y colores >> > spottypes <- readSpotTypes() ##usamos el default que es SpotTypes >> > RG$genes$Status <- controlStatus(spottypes, RG) >> > >> > ###definir el layour >> > printer= list(ngrid.r=1,ngrid.c=1,nspot.r=107,nspot.c=101) >> > RG$printer=structure(printer, class = "PrintLayout") >> > >> > ###normalize: background y normlize within >> > RGoriginal = RG >> > RG <- backgroundCorrect(RGoriginal, method="normexp", offset=50) >> > MA = normalizeWithinArrays(RG,method="loess",bc.method="none") >> > >> > ##escoger los genes nadamas >> > geneIndex = which(MA$genes$Status=="gene") >> > >> > ##preparar el disenyo para fit >> > design = modelMatrix(targets,ref="WT") >> > >> > ###ver todos los MA plots como QC: todos OK! >> > for(i in 1:6){ >> >> > > plotMA(MA[geneIndex,i],main=targets$Name[i],ylim=range(MA$M[ge neIndex,])) >> > scan() >> > } >> > >> > ###estimar los paramteros usando eBayes (moderated t-stat) y calcular >> > FDR >> > fit1 = lmFit(MA,design) >> > fit1 = eBayes(fit1) >> > tt=topTable(fit1,adjust="fdr",number=3000) >> > tt=tt[tt$P.Value<=0.01,] >> > >> > ###Escoger genes para incluir en las graficas >> > Index=as.numeric(rownames(tt)) >> > interestGenes <- >> > >> > c("CHS3","SLT2","PIR3","ECM4","ECM13","SPI1","ORF:YHR097C","MYO1" ,"PIR3","HOG1","SWE1") >> > interestIndex <- lapply(interestGenes,function(x) >> > which(MA$genes$GeneName==x)) >> > names(interestIndex) <- interestGenes >> > >> > ##MA-PLOT >> > >> > plot(fit1$Amean[geneIndex],fit1$coef[geneIndex],pch=".",col="grey ",ylab="M",xlab="A") >> > points(fit1$Amean[Index],fit1$coef[Index],pch=16,col="blue",cex=.5) >> > for(i in seq(along=interestGenes)){ >> > >> > >> > text(fit1$Amean[interestIndex[[i]]],fit1$coef[interestIndex[[i]]] ,interestGenes[i],cex=.5) >> > } >> > >> > ##VOLCANO >> > >> > plot(fit1$coef[geneIndex],-log10(fit1$p.value[geneIndex]),pch="." ,col="grey",xlab="M",ylab="-log_10 >> > p-value") >> > >> > points(fit1$coef[Index],-log10(fit1$p.value[Index]),pch=16,col="b lue",cex=.5) >> > for(i in seq(along=interestGenes)){ >> > >> > >> > text(fit1$coef[interestIndex[[i]]],-log10(fit1$p.value[interestIn dex[[i]]]),interestGenes[i],cex=.5) >> > } >> > >> > >> > ###hacer xls file >> > todos = topTable(fit1,adjust="fdr",number=10807) >> > write.csv(todos,file="myo1.csv") >> > -- >> > Jos? F. Rodr?guez Qui?ones >> > Graduate student >> > School of Medicine >> > Medical Sciences Campus >> > University of Puerto Rico >> > >> > [[alternative HTML version deleted]] >> > >> > >> > _______________________________________________ >> > 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 >> > > > > > -- > Jos? F. Rodr?guez Qui?ones > Graduate student > School of Medicine > Medical Sciences Campus > University of Puerto Rico

On Tue, Sep 30, 2008 at 11:39 AM, Jose Francisco Rodriguez <francquimico at="" gmail.com=""> wrote: > Hi: > Actually it worked using this command: > > write.csv(MA$M,file="Mvalues.csv") > > Is this OK? > > Thank You for your help!!!! Glad to hear you got it working. For future reference, it is best if you keep replies on the list so that everyone benefits from the questions and their answers. Also, though it wasn't important for this message, remember to ALWAYS include the output of sessionInfo() when writing a new message to the list. Sean > On Tue, Sep 30, 2008 at 11:29 AM, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: >> >> On Tue, Sep 30, 2008 at 10:47 AM, Jose Francisco Rodriguez >> <francquimico at="" gmail.com=""> wrote: >> > Hi: >> > Thank You! I applied this command (MA$M) and obtained the values, but >> > I'm >> > having problems creating an excel table for those. I suppose that I >> > should >> > use write.table, but I dont know how to put the command without giving >> > me an >> > error. Sorry but I'm still learning. >> >> What have you tried? And what error do you get? >> >> Sean >> >> >> > On 9/30/08, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: >> >> >> >> On Tue, Sep 30, 2008 at 9:42 AM, Jose Francisco Rodriguez >> >> <francquimico at="" gmail.com=""> wrote: >> >> > Hi: >> >> > I performed a microarray (5 biological replicates) and analyzed using >> >> > Limma. The software gives me the M value as a combination from the 5 >> >> > biological replicates microarray. In order to submit the data to GEO >> >> > database we need the M-value for each array ( 5 M-values). How can I >> >> > do >> >> > that? I'll appreciate your help. >> >> >> >> MA$M contains the M values for each array. >> >> >> >> Sean >> >> >> >> > Below there is a copy of the sequence for the analysis: >> >> > >> >> > library(limma) >> >> > library(geneplotter) >> >> > >> >> > ###Lee targets y image files >> >> > targets=readTargets("maTargetFKS12.txt") >> >> > >> >> > RG=read.maimages(files=cbind(targets$FileNameCy3,targets$FileNameCy5), >> >> > source="imagene") >> >> > >> >> > ##lear gal >> >> > gal=readGAL("013384_D_20050601.gal") >> >> > gal=gal[1:nrow(RG$R),] >> >> > ##asignar el gal >> >> > RG$genes=gal >> >> > >> >> > ###definir los Spot Types y colores >> >> > spottypes <- readSpotTypes() ##usamos el default que es SpotTypes >> >> > RG$genes$Status <- controlStatus(spottypes, RG) >> >> > >> >> > ###definir el layour >> >> > printer= list(ngrid.r=1,ngrid.c=1,nspot.r=107,nspot.c=101) >> >> > RG$printer=structure(printer, class = "PrintLayout") >> >> > >> >> > ###normalize: background y normlize within >> >> > RGoriginal = RG >> >> > RG <- backgroundCorrect(RGoriginal, method="normexp", offset=50) >> >> > MA = normalizeWithinArrays(RG,method="loess",bc.method="none") >> >> > >> >> > ##escoger los genes nadamas >> >> > geneIndex = which(MA$genes$Status=="gene") >> >> > >> >> > ##preparar el disenyo para fit >> >> > design = modelMatrix(targets,ref="WT") >> >> > >> >> > ###ver todos los MA plots como QC: todos OK! >> >> > for(i in 1:6){ >> >> >> >> > > >> >> > > plotMA(MA[geneIndex,i],main=targets$Name[i],ylim=range(MA$M [geneIndex,])) >> >> > scan() >> >> > } >> >> > >> >> > ###estimar los paramteros usando eBayes (moderated t-stat) y calcular >> >> > FDR >> >> > fit1 = lmFit(MA,design) >> >> > fit1 = eBayes(fit1) >> >> > tt=topTable(fit1,adjust="fdr",number=3000) >> >> > tt=tt[tt$P.Value<=0.01,] >> >> > >> >> > ###Escoger genes para incluir en las graficas >> >> > Index=as.numeric(rownames(tt)) >> >> > interestGenes <- >> >> > >> >> > >> >> > c("CHS3","SLT2","PIR3","ECM4","ECM13","SPI1","ORF:YHR097C","MY O1","PIR3","HOG1","SWE1") >> >> > interestIndex <- lapply(interestGenes,function(x) >> >> > which(MA$genes$GeneName==x)) >> >> > names(interestIndex) <- interestGenes >> >> > >> >> > ##MA-PLOT >> >> > >> >> > >> >> > plot(fit1$Amean[geneIndex],fit1$coef[geneIndex],pch=".",col="g rey",ylab="M",xlab="A") >> >> > points(fit1$Amean[Index],fit1$coef[Index],pch=16,col="blue",cex=.5) >> >> > for(i in seq(along=interestGenes)){ >> >> > >> >> > >> >> > >> >> > text(fit1$Amean[interestIndex[[i]]],fit1$coef[interestIndex[[i ]]],interestGenes[i],cex=.5) >> >> > } >> >> > >> >> > ##VOLCANO >> >> > >> >> > >> >> > plot(fit1$coef[geneIndex],-log10(fit1$p.value[geneIndex]),pch= ".",col="grey",xlab="M",ylab="-log_10 >> >> > p-value") >> >> > >> >> > >> >> > points(fit1$coef[Index],-log10(fit1$p.value[Index]),pch=16,col ="blue",cex=.5) >> >> > for(i in seq(along=interestGenes)){ >> >> > >> >> > >> >> > >> >> > text(fit1$coef[interestIndex[[i]]],-log10(fit1$p.value[interes tIndex[[i]]]),interestGenes[i],cex=.5) >> >> > } >> >> > >> >> > >> >> > ###hacer xls file >> >> > todos = topTable(fit1,adjust="fdr",number=10807) >> >> > write.csv(todos,file="myo1.csv") >> >> > -- >> >> > Jos? F. Rodr?guez Qui?ones >> >> > Graduate student >> >> > School of Medicine >> >> > Medical Sciences Campus >> >> > University of Puerto Rico >> >> > >> >> > [[alternative HTML version deleted]] >> >> > >> >> > >> >> > _______________________________________________ >> >> > 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 >> >> > >> > >> > >> > >> > -- >> > Jos? F. Rodr?guez Qui?ones >> > Graduate student >> > School of Medicine >> > Medical Sciences Campus >> > University of Puerto Rico > > > > -- > Jos? F. Rodr?guez Qui?ones > Graduate student > School of Medicine > Medical Sciences Campus > University of Puerto Rico >