Francesco, Thanks so much for your reply. Have you ever tried to normalize using .gpr files derived fom Genepix and if so what weights for spots. If so, is there any specific difference relative to analyzing feature extraction files? Can you find any improvements? Does it make sense to collapse replicate spot data prior to normalization or prior to applying the lmfit function or shoul tests be applied to individual spots? Have you tested not subtracting background? Lauro Lauro Sumoy Microarray Laboratory Bioinformatics and Genomics Program Center for Genomic Regulation (CRG) Dr. Aiguader 88 08003 Barcelona Spain Office Phone: +34-93-316-0125 CRG Fax: +34-93-316-0099 e-mail: lauro.sumoy at crg.es http://www.crg.es --------------------------- Microarray Laboratory Phone: +34-93-316-0126 Microarray Services Office: +34-93-316-0272 Microarray Bioinformatics Office: +34-93-316-0238 Program Secretarial Office: +34-93-316-0110 --------------------------- *****10 FELLOWSHIPS AVAILABLE***** TO CARRY OUT PhD STUDIES AT THE CENTER FOR GENOMIC REGULATION, FUNDED BY "LA CAIXA" INFORMATION @ http://pasteur.crg.es/portal/page/portal/Internet/05_TRABCONS/HIDE- PHD/4 1405C07B7E699F9E04012AC0E0155F7 -----Mensaje original----- De: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] En nombre de Francesco Favero Enviado el: lunes, 04 de febrero de 2008 12:32 Para: Bioconductor Asunto: Re: [BioC] Agilent miRNA one color platform analysis Dear Lauro, I'm happy some other people is interested in Agilent miRNA microarrays. We are using this platform for some time now, and I believe I managed to have some good results with the limma package. If you are using the Feature Extraction from Agilent you should just care a good function to weighting the spots. If you read the FE manual you see you have two parameters WellAboveBG and isPosAndSignif. Personally I prefer isPosAndSignif, because is less restrictive (Well above is isPosAndSignif plus other tests...). Than you have to pay attention to the "one-channel" problem with the Agilent microarray in limma. you can find some post in the mailing list about this matter. Background Correction we are using normexp, with an adequate offset. Is proved that normexp is a good method and it's suitable in our case, in fact in miRNA we have a lot of low intensity spots, normexp + offset fix it if this is the case. Than the normalisation.. this is the most problematic part. We find out that VSN is the best normalisation for us. I suggest you to have a look at : Davidson T.S., Johnson C.D. and Andruss B.F. "Analyzing micro-RNA expression using microarrays" Methods in Enzymology 411(1):14-34, 2006. Best regards Francesco [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor

Dear Francesco, I will follow up on your e-mail regarding Agilent miRNA single channel microarray normalization. As I had mentined at the moment we are working with genepix data, although we may play around with Agilent feature extraction as well we feel more comfortable with managing genepix extracted gpr files with the irregular feature finding option. I am able to load data from single channel gpr files by asking it to load the green channel data twice. library(limma) targets<-readTargets(file="Agilent_miRNA_Targets.txt",sep="\t") RG <- read.maimages(targets,source="genepix", columns=list(R="F532 Median",G="F532 Median",Rb="B532 Mean",Gb="B532 Mean")) vG <- normalizeBetweenArrays(RG$G,method="vsn") Regarding use of normexp along with vsn, the limma manual says vsn should be applied to non-background subtracted data. Even so I tried do do this. After applying normexp to single channel data (no offset applied) followed by vsn, the resulting log2intensities are way off (no values under 12!!!): BSubRG <- backgroundCorrect(RG, method="normexp") vG <- normalizeBetweenArrays(BSubRG$G,method="vsn") What offset values do you suggest using when applying normexp background subtraction? Do you actually use normexp along with vsn or is it that you apply normexp along with quantile normalization or vsn on its own? Thanks again for your input. Other people's input would be appreciated. Lauro Lauro Sumoy Microarray Laboratory Bioinformatics and Genomics Program Center for Genomic Regulation (CRG) Dr. Aiguader 88 08003 Barcelona Spain Office Phone: +34-93-316-0125 CRG Fax: +34-93-316-0099 e-mail: lauro.sumoy at crg.es http://www.crg.es -----Mensaje original----- De: bioconductor-bounces at stat.math.ethz.ch [mailto:bioconductor-bounces at stat.math.ethz.ch] En nombre de Francesco Favero Enviado el: lunes, 04 de febrero de 2008 12:32 Para: Bioconductor Asunto: Re: [BioC] Agilent miRNA one color platform analysis Dear Lauro, I'm happy some other people is interested in Agilent miRNA microarrays. We are using this platform for some time now, and I believe I managed to have some good results with the limma package. If you are using the Feature Extraction from Agilent you should just care a good function to weighting the spots. If you read the FE manual you see you have two parameters WellAboveBG and isPosAndSignif. Personally I prefer isPosAndSignif, because is less restrictive (Well above is isPosAndSignif plus other tests...). Than you have to pay attention to the "one-channel" problem with the Agilent microarray in limma. you can find some post in the mailing list about this matter. Background Correction we are using normexp, with an adequate offset. Is proved that normexp is a good method and it's suitable in our case, in fact in miRNA we have a lot of low intensity spots, normexp + offset fix it if this is the case. Than the normalisation.. this is the most problematic part. We find out that VSN is the best normalisation for us. I suggest you to have a look at : Davidson T.S., Johnson C.D. and Andruss B.F. "Analyzing micro-RNA expression using microarrays" Methods in Enzymology 411(1):14-34, 2006. Best regards Francesco [[alternative HTML version deleted]] _______________________________________________ Bioconductor mailing list Bioconductor at stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/bioconductor Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor