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Hi
I'm a newbie to Bioconductor and am trying to implement QC into a
pipeline I am building for analysing Agilent data.
I have followed the very good code given in 'Single channel analysis
of Agilent microarray data with Limma' and implemented it in Python
with the R interface (RPy2) and have been able to generate an output
file of differential gene expression data. However, I want to be able
to look at MA plots for the normalized data prior to producing the
differential data so that I can assess whether normalization has done
its job. I also want to be able to save each of the normalized arrays
to files for other analyses. I have looked at the outputs of the
various R steps but have been unable to identify where the relevant M
and A data are. I have pasted the relevant part of my code below.
Any help/advice with regard to my two queries would be greatly
appreciated.
Thanks
Lesley
-----------------------------------------
robjects.r.library("limma")
#Normalization
robjects.r('targets <- readTargets("target.txt")') #Read in tab-
delimited 'target.txt' file (contains names of raw data file and
corresponding sample information)
robjects.r('x <- read.maimages(targets, path="microarray_raw_data/",
source="agilent",green.only=TRUE)') #Load data into a list object
robjects.r('y <- backgroundCorrect(x, method="normexp", offset=16)')
#Subtract the background
robjects.r('y <- normalizeBetweenArrays(y, method="quantile")')
#Normalize the green channel between the arrays; quantile
normalization is part of RMA and normalizes a batch of arrays to make
further comparisons meaningful
robjects.r('y.ave <- avereps(y, ID=y$genes$ProbeName)') #Use the
avereps function to average replicate spots
-- output of sessionInfo():
R version 2.15.0 (2012-03-30)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.utf8 LC_NUMERIC=C
[3] LC_TIME=en_US.utf8 LC_COLLATE=en_US.utf8
[5] LC_MONETARY=en_US.utf8 LC_MESSAGES=en_US.utf8
[7] LC_PAPER=C LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.utf8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
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