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 -- Sent via the guest posting facility at bioconductor.org.

Dear Lesley, M and A components are, by definition, only for two colour data. So you cannot expect to find them in any object from your single channel data. However, you will find that you can simply use: plotMA(y) See the help page for that function. Best wishes Gordon --------------------------------------------- Professor Gordon K Smyth, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Vic 3052, Australia. http://www.wehi.edu.au http://www.statsci.org/smyth On Wed, 4 Jul 2012, Lesley Hoyles [guest] wrote: > > 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 ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}