Seeking help to remove control spots in single channel normalization
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@prashantha-hebbar-kiradi-mu-mlsc-3159
Last seen 9.6 years ago
Dear Friends, I am working on single channel normalization for Agilent 244K chip data using Limma package. I refered the communication happend between Gordon and Abhilash about single channel normalization. I able perform it. But, not able to get the gene list soon after the normalization as we get in dual channel analysis. I am able to get the gene list in topTable stage. But I do not want gene list at the end of the analysis. Because I want remove the control spots soon after the normalization as we do for dual color. Following are the steps which I followed to perform single channel normalization, >library(limma) > target<-readTargets("/home/mlscrh2/MData/target.txt") > RG<-read.maimages(target$FileName, source="agilent", path="/home/mlscrh2/PrakrathiData", columns = list(G="gMeanSignal", Gb="gBGMeanSignal",R="gMedianSignal",Rb="gBGMedianSignal"),annotation= c("Row", "Col", "ProbeUID","ProbeName", "GeneName")) > Ggene<-backgroundCorrect(RG$G,method='normexp') > MA.q <- normalizeBetweenArrays(Ggene, method="quantile") > design = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1) > fit = lmFit(MA.q,design)> ebFit <- eBayes(fit) > a <- topTable(ebFit,genelist = RG$genes,adjust="fdr",n=300000) I tried to incorporate genelist in the stage of background correction and normalization, but ends up with an error. So can you please suggest me, How to remove control spots soon after normalization in single channel analysis? Thanking you in anticipation. Regards, Prashantha ###################################################################### Attention: This e-mail message is privileged and confidential. If you are not the intended recipient please delete the message and notify the sender. Any views or opinions presented are solely those of the author. ###################################################################### [[alternative HTML version deleted]]
Normalization limma Normalization limma • 1.0k views
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Leon Yee ▴ 110
@leon-yee-3088
Last seen 9.6 years ago
Hi, Prashantha Hebbar Kiradi [MU-MLSC] wrote: > Dear Friends, > > I am working on single channel normalization for Agilent 244K chip data using Limma package. I refered the communication happend between Gordon and Abhilash about single channel normalization. I able perform it. But, not able to get the gene list soon after the normalization as we get in dual channel analysis. > > I am able to get the gene list in topTable stage. But I do not want gene list at the end of the analysis. Because I want remove the control spots soon after the normalization as we do for dual color. > > Following are the steps which I followed to perform single channel normalization, >> library(limma) >> target<-readTargets("/home/mlscrh2/MData/target.txt") >> RG<-read.maimages(target$FileName, source="agilent", path="/home/mlscrh2/PrakrathiData", columns = list(G="gMeanSignal", Gb="gBGMeanSignal",R="gMedianSignal",Rb="gBGMedianSignal"),annotation= c("Row", "Col", "ProbeUID","ProbeName", "GeneName")) >> Ggene<-backgroundCorrect(RG$G,method='normexp') >> MA.q <- normalizeBetweenArrays(Ggene, method="quantile") >> design = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1) >> fit = lmFit(MA.q,design)> ebFit <- eBayes(fit) >> a <- topTable(ebFit,genelist = RG$genes,adjust="fdr",n=300000) > > I tried to incorporate genelist in the stage of background correction and normalization, but ends up with an error. > So can you please suggest me, How to remove control spots soon after normalization in single channel analysis? The Feature Extraction file of Agilent array will contain a column called "ControlType", so if you use read.maimages with annotation= c("Row", "Col", "ProbeUID","ProbeName", "GeneName", "ControlType"), you can filter out the control spots by "ControlType": 0 means non-control. HTH Leon
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Dear Lee, I am thankful to your response. Yes, as you said control information will be there in ControlTypes column of featured extract data. But the problem is when I give following command for background correction Ggene<-backgroundCorrect(RG$G,method='normexp') #I referred this command in mail list Ggene variable will not have RG$genes information (i.e. gene list info). If I get RG$genes information in next steps (after inter array normalization) then only it is possible to remove control spots. So, now my question is If I use directly RG in my background correction (for example: Ggene<-backgroundCorrect(RG,method='normexp')), will it create any problem? In this case, RG$G-RG$Gb and RG$R-RG$Rb will be done right? So, analysis is meaningful right? If you make me clear in this I think I will not have any further problem in removing Control spots after inter array normalization. Sorry for making you to write me again. Regards, Prashantha -----Original Message----- From: Leon Yee [mailto:yee.leon@gmail.com] Sent: Monday, January 05, 2009 6:37 PM To: Prashantha Hebbar Kiradi [MU-MLSC] Cc: bioconductor at stat.math.ethz.ch Subject: Re: [BioC] Seeking help to remove control spots in single channel normalization Hi, Prashantha Hebbar Kiradi [MU-MLSC] wrote: > Dear Friends, > > I am working on single channel normalization for Agilent 244K chip data using Limma package. I refered the communication happend between Gordon and Abhilash about single channel normalization. I able perform it. But, not able to get the gene list soon after the normalization as we get in dual channel analysis. > > I am able to get the gene list in topTable stage. But I do not want gene list at the end of the analysis. Because I want remove the control spots soon after the normalization as we do for dual color. > > Following are the steps which I followed to perform single channel normalization, >> library(limma) >> target<-readTargets("/home/mlscrh2/MData/target.txt") >> RG<-read.maimages(target$FileName, source="agilent", path="/home/mlscrh2/PrakrathiData", columns = list(G="gMeanSignal", Gb="gBGMeanSignal",R="gMedianSignal",Rb="gBGMedianSignal"),annotation= c("Row", "Col", "ProbeUID","ProbeName", "GeneName")) >> Ggene<-backgroundCorrect(RG$G,method='normexp') >> MA.q <- normalizeBetweenArrays(Ggene, method="quantile") >> design = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1) >> fit = lmFit(MA.q,design)> ebFit <- eBayes(fit) >> a <- topTable(ebFit,genelist = RG$genes,adjust="fdr",n=300000) > > I tried to incorporate genelist in the stage of background correction and normalization, but ends up with an error. > So can you please suggest me, How to remove control spots soon after normalization in single channel analysis? The Feature Extraction file of Agilent array will contain a column called "ControlType", so if you use read.maimages with annotation= c("Row", "Col", "ProbeUID","ProbeName", "GeneName", "ControlType"), you can filter out the control spots by "ControlType": 0 means non-control. HTH Leon ###################################################################### Attention:\ This e-mail message is privileged and confid...{{dropped:7}}
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Hi Prashantha, >Ggene<-backgroundCorrect(RG$G,method='normexp') #I referred this command >in mail list > >Ggene variable will not have RG$genes information (i.e. gene list info). If >I get RG$genes information in next steps (after inter array normalization) >then only it is possible to remove control spots. > >So, now my question is If I use directly RG in my background correction (for >example: Ggene<-backgroundCorrect(RG,method='normexp')), will it create any >problem? In this case, RG$G-RG$Gb and RG$R-RG$Rb will be done right? So, >analysis is meaningful right? If you give backgroundCorrect() only RG$G, it doesn't have RG$Gb for the background correction at all; I don't remember all of the details of the normexp method, but I thought it did use the background values. If so, your command is not doing what you thought it would be doing. The backgroundCorrect() function was meant to directly work on RGList objects, so I'm not sure why you thought it might not work correctly if you give it RG. HTH, Jenny >If you make me clear in this I think I will not have any further problem in >removing Control spots after inter array normalization. Sorry for making you >to write me again. > >Regards, > >Prashantha > >-----Original Message----- >From: Leon Yee [mailto:yee.leon at gmail.com] >Sent: Monday, January 05, 2009 6:37 PM >To: Prashantha Hebbar Kiradi [MU-MLSC] >Cc: bioconductor at stat.math.ethz.ch >Subject: Re: [BioC] Seeking help to remove control spots in single channel >normalization > >Hi, > >Prashantha Hebbar Kiradi [MU-MLSC] wrote: > > Dear Friends, > > > > I am working on single channel normalization for Agilent 244K chip data >using Limma package. I refered the communication happend between Gordon and >Abhilash about single channel normalization. I able perform it. But, not >able to get the gene list soon after the normalization as we get in dual >channel analysis. > > > > I am able to get the gene list in topTable stage. But I do not want gene >list at the end of the analysis. Because I want remove the control spots >soon after the normalization as we do for dual color. > > > > Following are the steps which I followed to perform single channel >normalization, > >> library(limma) > >> target<-readTargets("/home/mlscrh2/MData/target.txt") > >> RG<-read.maimages(target$FileName, source="agilent", >path="/home/mlscrh2/PrakrathiData", columns = list(G="gMeanSignal", >Gb="gBGMeanSignal",R="gMedianSignal",Rb="gBGMedianSignal"),annotation = >c("Row", "Col", "ProbeUID","ProbeName", "GeneName")) > >> Ggene<-backgroundCorrect(RG$G,method='normexp') > >> MA.q <- normalizeBetweenArrays(Ggene, method="quantile") > >> design = c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1) > >> fit = lmFit(MA.q,design)> ebFit <- eBayes(fit) > >> a <- topTable(ebFit,genelist = RG$genes,adjust="fdr",n=300000) > > > > I tried to incorporate genelist in the stage of background correction and >normalization, but ends up with an error. > > So can you please suggest me, How to remove control spots soon after >normalization in single channel analysis? > >The Feature Extraction file of Agilent array will contain a column >called "ControlType", so if you use read.maimages with >annotation= c("Row", "Col", "ProbeUID","ProbeName", "GeneName", >"ControlType"), you can filter out the control spots by "ControlType": >0 means non-control. > >HTH > >Leon > > >##################################################################### # >Attention:\ This e-mail message is privileged and confid...{{dropped:7}} > >_______________________________________________ >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 Jenny Drnevich, Ph.D. Functional Genomics Bioinformatics Specialist W.M. Keck Center for Comparative and Functional Genomics Roy J. Carver Biotechnology Center University of Illinois, Urbana-Champaign 330 ERML 1201 W. Gregory Dr. Urbana, IL 61801 USA ph: 217-244-7355 fax: 217-265-5066 e-mail: drnevich at illinois.edu
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Hi, Prashantha Prashantha Hebbar wrote: > Dear Lee, > > I am thankful to your response. Yes, as you said control information will be > there in ControlTypes column of featured extract data. But the problem is > when I give following command for background correction > > Ggene<-backgroundCorrect(RG$G,method='normexp') #I referred this command > in mail list > > Ggene variable will not have RG$genes information (i.e. gene list info). If Since the input of backgroundCorrect is RG$G, which don't contain RG$genes information, the result Ggene will surely not contain it. But you can still filter the control spot: Ggene[RG$genes$ControlType==0,] > I get RG$genes information in next steps (after inter array normalization) > then only it is possible to remove control spots. > > So, now my question is If I use directly RG in my background correction (for > example: Ggene<-backgroundCorrect(RG,method='normexp')), will it create any > problem? In this case, RG$G-RG$Gb and RG$R-RG$Rb will be done right? So, > analysis is meaningful right? I am not know anything about single channel array analysis. But if it is two-color array, backgroundCorrect(RG, method=whatever) is OK. Leon
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