Dear Huber,

As you directed, I tried arrayQualityMetrics. I used feature extracted txt
file  from Agilent  arrays and created RGList. It has given the following
error, I am wondering whether it is  because I didnot try to create
assayData, featureData and phenoData.
arrayQualityMetrics(expressionset = array1,
+ outdir = "array")
*The directory 'array' has been created.
Error in seq_len(ncol(x)) : argument must be non-negative

*What could I do to solve this problem?

Regards,
Abhilash


On Wed, Apr 16, 2008 at 11:07 PM, Wolfgang Huber <huber@ebi.ac.uk> wrote:

>
> Dear Abilash,
>
> you could try to run the arrayQualityMetrics report from the eponymous
> package (and preferably a current devel version [1]). The plots in there
> would help you to assess both whether there are gross problems in the
> data, and how much and what type of normalisation is needed.
>
> Adequate normalisation removes unwanted technical variation while it
> maintains the interesting biological signal in the data. The best way to
> assess whether this is goal is achieved is by looking at the behaviour
> of the controls that were done as part of the experiment.
>
> [1]
>
> http://www.bioconductor.org/packages/2.2/bioc/html/arrayQualityMetrics.html
>
> Best wishes
>  Wolfgang
>
> ------------------------------------------------------------------
> Wolfgang Huber  EBI/EMBL  Cambridge UK  http://www.ebi.ac.uk/huber
>
>
> 16/04/2008 18:02 Abhilash Venu a écrit
> > Hi all,
> >
> > I have a few questions. I am analyzing  20 experiments  performed  in
> > Agilent 4x44K. Tumor and adjacent normal were labeled with Cy5 and Cy3
> > respectively. The experiments include two dye swaps also. The feature
> > extraction were performed using Agilent feature extraction software
> version
> > 8.5. The txt files obtained after feature extraction, subjected to
> further
> > analysis by limma. The script has given below
> > txt_files <- dir(pattern=".txt")
> > RG<-read.maimages(txt_files, source="agilent")
> > design<-c(1,1,1,1,1,1,1,1,1,1,1,-1,1,1,1,1,1,1,1,-1) # this include two
> dye
> > swap also
> > Rgene<-backgroundCorrect(RG,method="normexp")
> > fit<-lmFit(Rgene,design)
> > fit<-eBayes(fit)
> > topTable(fit,adjust="fdr", number=100)
> >
> > As a result I get the format in which 'B' indicates what parameter? And
> is
> > this analysis method is enough to get a good data?
> > In Agilent arrays, I think generally it is normalized, in that case how
> the
> > data will be affected by normalizing it again using above method?
> > How can we decide which normalization methods should be used? Do you
> think
> > its always better to decide by plotting different graphs?
> >
> > Comments and answers will be appreciated.
> >
> > Regards,
> > Abhilash
> >
> >
>



-- 

Regards,
Abhilash

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