[BioC] single channel analysis

[David martin]

Ok thanks,
that would do the trick
On 04/29/2010 02:55 PM, Maciej Jończyk wrote:
> 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)
>>>>
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>>>
>>
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>>
>
>
>
>
> 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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