[BioC] single channel analysis

[Maciej Jończyk]

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
> >
>
> _______________________________________________
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> Search the archives:
http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>




Maciej Jończyk, MSc
Department of Plant Molecular Ecophysiology
Institute of Plant Experimental Biology
Faculty of Biology, University of Warsaw
02-096 Warszawa, Miecznikowa 1



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