[BioC] single channel analysis

[David martin]

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)
>>
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>