I have an Agilent data set , and normalized and read it using the code bellow , but the result is different from what the paper claim.
library("limma", lib.loc="~/R/win-library/3.1")
targets<-readTargets("target2.txt",row.names=1,sep="")
targets$Filenames[!file.exists(targets$Filenames)]
x <- read.maimages(targets$Filenames,source="agilent", green.only=TRUE)
y <- backgroundCorrect(x,method="normexp")
NormData<-normalizeBetweenArrays(y,method="quantile")
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written
by
nvayin •
You posted the same question to the Bioconductor mailing list, and it has been answered there.
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written
by
Gordon Smyth
I can not figure out why my result is different? i do not what GX gene spring is doing , what the paper said is :
stander Agilent normalize methods? I do not know what is?
Extracted data were analysed using GeneSpring GX
7.3.1 (Silicon Genetics, USA). Agilent standard scenario
normalizations for FE1-colour arrays were applied to all
data sets. A subset of genes for data interrogation was
generated that excluded spots of poor quality, and gene
probes that were expressed in <50% of samples.
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Do the paper provide code to get their result? Have you contacted the authors?
The paper used GX gene spring to normalize the data, I contact the authors but they are not replying
hello,
I have one query what type of data we put in target2.txt file...