[BioC] M values

[Sean Davis]

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","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="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]]
>> >> >
>> >> >
>> >> > _______________________________________________
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>> >> >
>> >
>> >
>> >
>> > --
>> > José F. Rodríguez Quiñones
>> > Graduate student
>> > School of Medicine
>> > Medical Sciences Campus
>> > University of Puerto Rico
>
>
>
> --
> José F. Rodríguez Quiñones
> Graduate student
> School of Medicine
> Medical Sciences Campus
> University of Puerto Rico
>