Hi Guillermo, On 6/21/2013 3:06 AM, Guillermo Marco Puche wrote: > Dear Gordon, > > Thank you for your answer. I'll look further into Agilent array image > files with limma. > > As I said the problem is that i'm not currently reading image from > Agilent array, but the text data file with marray library and loading it > into a maData object like this: Please note that the read.maimages function doesn't read image files - it reads in the same text files you are reading with read.Agilent. Your original question had to do with the 'correct' background correction to use for your Agilent array data. Gordon has therefore suggested that you use the 'normexp' method in limma. This does of course require you to switch to a different package, but limma tends to get better support than marray, so you might be wise to make the switch. But to your original point, you are asking a question that might not have a definitive answer. There is no 'best' way to do a background correction. There are methods that seem to do a reasonable job over a range of experiments, and if I understand correctly, this is why Gordon is suggesting you use normexp. But which method might be best for your particular situation will be difficult for anybody to predict. Best, Jim > > maData = read.Agilent(fnames=input , path=NULL, name.Gf = "gMedianSignal", name.Gb = "gBGMedianSignal", name.Rf = "rMedianSignal", name.Rb = "rBGMedianSignal", name.W= NULL, layout = NULL, gnames = NULL, targets = NULL, notes=NULL, skip=NULL, sep="\t", quote="\"", DEBUG=FALSE, info.id=NULL) > > > > >> On 06/20/2013 01:11 PM, Gordon K Smyth wrote: >>> Dera Guillermo, >>> >>> The usual process is to (1) background correct the foreground >>> intensities with respect to the background, then (2) normalize the >>> M-values (log-ratios). >>> >>> For an Agilent two colour array, I do this by: >>> >>> library(limma) >>> RG<- read.maimages(files, source="agilent") >>> RGb<- backgroundCorrect(RG, method="normexp") >>> MA<- normalizeWithinArrays(RGb, method="loess") >>> >>> although it is sometimes a good idea to remove positive control >>> probes before the normalization step. >>> >>> A recent example using this pipeline is: >>> >>> http://www.biomedcentral.com/1471-2105/14/165 >>> >>> Best wishes >>> Gordon >>> >>>> Date: Wed, 19 Jun 2013 22:38:34 +0200 >>>> From: Guillermo Marco Puche<guillermo.marco at="" sistemasgenomicos.com=""> >>>> To: "bioconductor at r-project.org"<bioconductor at="" r-project.org=""> >>>> Subject: [BioC] Normalize background on marray Agilent object >>>> >>>> Hello, >>>> >>>> I'm currently trying to normalize rBG values for a marray object. >>>> Data origin is Agilent dual channel array. I've loaded information with >>>> readAgilent() function. >>>> >>>> What's the correct way to normalize the data? I would like to normalize >>>> background information first maNorm function manual isn't very >>>> clarifying for me. >>>> >>>> Thanks ! >>>> >>>> Best regards, >>>> Guillermo. >>> >>> ______________________________________________________________________ >>> The information in this email is confidential and intended solely for >>> the addressee. >>> You must not disclose, forward, print or use it without the >>> permission of the sender. >>> ______________________________________________________________________ > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099