process one color microarray
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@paz-tapia-ramirez-4783
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Hello, I have a question. I'm Working with one-color microarrays . I worked in 4 conditions different and each condition I have 4 replicates. Now, my question is when I load the files to Bioconductor, I load as follows: my.filenames <- c ("Condic1_repl1.txt", "Condic1_repl2.txt", "Condic1_repl3.txt", "Condic1_repl4.txt", "Condic2_repl1.txt", "Condic2_repl2.txt", "Condic2_repl3.txt", "Condic2_repl4.txt", "Condic3_repl1.txt" "Condic3_repl2.txt", "Condic3_repl3.txt", "Condic3_repl4.txt", "Condic3_repl1.txt", "Condic3_repl2.txt", "Condic3_repl3.txt", "Condic3_repl4.txt") Subsequently, I realize the normalization procedure and some statistical calculations: one.col1 <-list (R = "gMeanSignal" G = "gProcessedSignal" Rb = "gBGMedianSignal", Gb = "gProcessedBackground") RG1 <- read.maimages (my.filenames, source = "agilent", columns = one.col1, dec =".") RG1 <- backgroundCorrect (RG1, method = "half", offset = 50) MA1 <- normalizeBetweenArrays (RG1, method = "quantile") fit1 <- lmFit (MA1, design = NULL) fit1 <- eBay (fit1) But my question is: how I can specify to bioconductor which files correspond to Control, or which correspond to microarrays with treatment? Regards, Paz [[alternative HTML version deleted]]
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@james-w-macdonald-5106
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Hi Paz, On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > Hello, I have a question. I'm Working with one-color microarrays . I worked in 4 conditions different and each condition I have 4 replicates. Now, my question is when I load the files to Bioconductor, I load as follows: > my.filenames<- c ("Condic1_repl1.txt", > "Condic1_repl2.txt", > "Condic1_repl3.txt", > "Condic1_repl4.txt", > "Condic2_repl1.txt", > "Condic2_repl2.txt", > "Condic2_repl3.txt", > "Condic2_repl4.txt", > "Condic3_repl1.txt" > "Condic3_repl2.txt", > "Condic3_repl3.txt", > "Condic3_repl4.txt", > "Condic3_repl1.txt", > "Condic3_repl2.txt", > "Condic3_repl3.txt", > "Condic3_repl4.txt") > > Subsequently, I realize the normalization procedure and some statistical calculations: > one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > RG1<- read.maimages (my.filenames, source = "agilent", columns = one.col1, dec =".") > RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > MA1<- normalizeBetweenArrays (RG1, method = "quantile") > fit1<- lmFit (MA1, design = NULL) The design matrix indicates to lmFit() what is control and what is treatment. When you specify a NULL design matrix, lmFit() will just use a vector of 1s, which would be fine if you had two-color chips and no dye-swaps. Otherwise, you are just testing the hypothesis that the average expression of all samples is not equal to zero (which obviously isn't correct). So if you have four conditions with four replicates (and I am assuming here that they are Biological replicates, not just different aliquots of the same sample), you want a design matrix with four columns. The simplest such design matrix (to me, anyway), computes the mean expression for each group, and then you can just make the comparisons you want. cond <- factor(rep(1:4, each = 4)) design <- model.matrix(~ 0 + cond) colnames(design) <- c("trt1","trt2","trt3","trt4") contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, trt4-trt2, trt4-trt3, levels = design) fit <- lmFit(Ma1, design) fit1 <- contrasts.fit(fit, contrast) fit1 <- eBayes(fit1) which will make all possible comparisons. Alternatively, if you just want to compare all treatments to a control (and assuming your control is trt1). design <- model.matrix(~cond) fit <- lmFit(Ma1, design) fit1 <- eBayes(fit) In this case, all coefficients in the model will be e.g., trt2-trt1, trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. Best, Jim > fit1<- eBay (fit1) > > But my question is: how I can specify to bioconductor which files correspond to Control, or which correspond to microarrays with treatment? > > Regards, Paz > > [[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 Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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Hi Paz, hi Jim, I think there is a problem here. Jim, you have given detailed explanation of making design matrix and do various comparisons, but they are based on the assumption that the input given to lmFit is log expression values from one color array. The problem is that, based on Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to processed signals of the green channel (of course background corrected and quantile normalized). I doubt very much that that piece of codes does what Paz wants to do. However, it's also possible that I missed something. Best regards, Yong James W. MacDonald wrote: > Hi Paz, > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: >> Hello, I have a question. I'm Working with one-color microarrays . I >> worked in 4 conditions different and each condition I have 4 >> replicates. Now, my question is when I load the files to >> Bioconductor, I load as follows: >> my.filenames<- c ("Condic1_repl1.txt", >> "Condic1_repl2.txt", >> "Condic1_repl3.txt", >> "Condic1_repl4.txt", >> "Condic2_repl1.txt", >> "Condic2_repl2.txt", >> "Condic2_repl3.txt", >> "Condic2_repl4.txt", >> "Condic3_repl1.txt" >> "Condic3_repl2.txt", >> "Condic3_repl3.txt", >> "Condic3_repl4.txt", >> "Condic3_repl1.txt", >> "Condic3_repl2.txt", >> "Condic3_repl3.txt", >> "Condic3_repl4.txt") >> >> Subsequently, I realize the normalization procedure and some >> statistical calculations: >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") >> RG1<- read.maimages (my.filenames, source = "agilent", columns = >> one.col1, dec =".") >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") >> fit1<- lmFit (MA1, design = NULL) > > The design matrix indicates to lmFit() what is control and what is > treatment. When you specify a NULL design matrix, lmFit() will just use > a vector of 1s, which would be fine if you had two-color chips and no > dye-swaps. Otherwise, you are just testing the hypothesis that the > average expression of all samples is not equal to zero (which obviously > isn't correct). > > So if you have four conditions with four replicates (and I am assuming > here that they are Biological replicates, not just different aliquots of > the same sample), you want a design matrix with four columns. The > simplest such design matrix (to me, anyway), computes the mean > expression for each group, and then you can just make the comparisons > you want. > > cond <- factor(rep(1:4, each = 4)) > design <- model.matrix(~ 0 + cond) > colnames(design) <- c("trt1","trt2","trt3","trt4") > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, > trt4-trt2, trt4-trt3, levels = design) > fit <- lmFit(Ma1, design) > fit1 <- contrasts.fit(fit, contrast) > fit1 <- eBayes(fit1) > > which will make all possible comparisons. > > Alternatively, if you just want to compare all treatments to a control > (and assuming your control is trt1). > > design <- model.matrix(~cond) > fit <- lmFit(Ma1, design) > fit1 <- eBayes(fit) > > In this case, all coefficients in the model will be e.g., trt2-trt1, > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. > > Best, > > Jim > > > >> fit1<- eBay (fit1) >> >> But my question is: how I can specify to bioconductor which files >> correspond to Control, or which correspond to microarrays with >> treatment? >> >> Regards, Paz >> >> [[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 >
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Hi Yong, What do you recommend? Best Regards, Paz > Date: Fri, 16 Sep 2011 13:32:30 +0200 > From: yong.li@zbsa.uni-freiburg.de > To: jmacdon@med.umich.edu > CC: verotapia@alumnos.utalca.cl; bioconductor@r-project.org > Subject: Re: [BioC] process one color microarray > > Hi Paz, hi Jim, > > I think there is a problem here. Jim, you have given detailed > explanation of making design matrix and do various comparisons, but they > are based on the assumption that the input given to lmFit is log > expression values from one color array. The problem is that, based on > Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to > processed signals of the green channel (of course background corrected > and quantile normalized). I doubt very much that that piece of codes > does what Paz wants to do. However, it's also possible that I missed > something. > > Best regards, > Yong > > James W. MacDonald wrote: > > Hi Paz, > > > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > >> Hello, I have a question. I'm Working with one-color microarrays . I > >> worked in 4 conditions different and each condition I have 4 > >> replicates. Now, my question is when I load the files to > >> Bioconductor, I load as follows: > >> my.filenames<- c ("Condic1_repl1.txt", > >> "Condic1_repl2.txt", > >> "Condic1_repl3.txt", > >> "Condic1_repl4.txt", > >> "Condic2_repl1.txt", > >> "Condic2_repl2.txt", > >> "Condic2_repl3.txt", > >> "Condic2_repl4.txt", > >> "Condic3_repl1.txt" > >> "Condic3_repl2.txt", > >> "Condic3_repl3.txt", > >> "Condic3_repl4.txt", > >> "Condic3_repl1.txt", > >> "Condic3_repl2.txt", > >> "Condic3_repl3.txt", > >> "Condic3_repl4.txt") > >> > >> Subsequently, I realize the normalization procedure and some > >> statistical calculations: > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > >> RG1<- read.maimages (my.filenames, source = "agilent", columns = > >> one.col1, dec =".") > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") > >> fit1<- lmFit (MA1, design = NULL) > > > > The design matrix indicates to lmFit() what is control and what is > > treatment. When you specify a NULL design matrix, lmFit() will just use > > a vector of 1s, which would be fine if you had two-color chips and no > > dye-swaps. Otherwise, you are just testing the hypothesis that the > > average expression of all samples is not equal to zero (which obviously > > isn't correct). > > > > So if you have four conditions with four replicates (and I am assuming > > here that they are Biological replicates, not just different aliquots of > > the same sample), you want a design matrix with four columns. The > > simplest such design matrix (to me, anyway), computes the mean > > expression for each group, and then you can just make the comparisons > > you want. > > > > cond <- factor(rep(1:4, each = 4)) > > design <- model.matrix(~ 0 + cond) > > colnames(design) <- c("trt1","trt2","trt3","trt4") > > > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, > > trt4-trt2, trt4-trt3, levels = design) > > fit <- lmFit(Ma1, design) > > fit1 <- contrasts.fit(fit, contrast) > > fit1 <- eBayes(fit1) > > > > which will make all possible comparisons. > > > > Alternatively, if you just want to compare all treatments to a control > > (and assuming your control is trt1). > > > > design <- model.matrix(~cond) > > fit <- lmFit(Ma1, design) > > fit1 <- eBayes(fit) > > > > In this case, all coefficients in the model will be e.g., trt2-trt1, > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. > > > > Best, > > > > Jim > > > > > > > >> fit1<- eBay (fit1) > >> > >> But my question is: how I can specify to bioconductor which files > >> correspond to Control, or which correspond to microarrays with > >> treatment? > >> > >> Regards, Paz > >> > >> [[alternative HTML version deleted]] > >> > >> _______________________________________________ > >> Bioconductor mailing list > >> Bioconductor@r-project.org > >> https://stat.ethz.ch/mailman/listinfo/bioconductor > >> Search the archives: > >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > [[alternative HTML version deleted]]
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Hi JIM. When you say "trt1" "trt2" you mean the column GErep in the targets file ? > Date: Fri, 16 Sep 2011 13:32:30 +0200 > From: yong.li@zbsa.uni-freiburg.de > To: jmacdon@med.umich.edu > CC: verotapia@alumnos.utalca.cl; bioconductor@r-project.org > Subject: Re: [BioC] process one color microarray > > Hi Paz, hi Jim, > > I think there is a problem here. Jim, you have given detailed > explanation of making design matrix and do various comparisons, but they > are based on the assumption that the input given to lmFit is log > expression values from one color array. The problem is that, based on > Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to > processed signals of the green channel (of course background corrected > and quantile normalized). I doubt very much that that piece of codes > does what Paz wants to do. However, it's also possible that I missed > something. > > Best regards, > Yong > > James W. MacDonald wrote: > > Hi Paz, > > > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > >> Hello, I have a question. I'm Working with one-color microarrays . I > >> worked in 4 conditions different and each condition I have 4 > >> replicates. Now, my question is when I load the files to > >> Bioconductor, I load as follows: > >> my.filenames<- c ("Condic1_repl1.txt", > >> "Condic1_repl2.txt", > >> "Condic1_repl3.txt", > >> "Condic1_repl4.txt", > >> "Condic2_repl1.txt", > >> "Condic2_repl2.txt", > >> "Condic2_repl3.txt", > >> "Condic2_repl4.txt", > >> "Condic3_repl1.txt" > >> "Condic3_repl2.txt", > >> "Condic3_repl3.txt", > >> "Condic3_repl4.txt", > >> "Condic3_repl1.txt", > >> "Condic3_repl2.txt", > >> "Condic3_repl3.txt", > >> "Condic3_repl4.txt") > >> > >> Subsequently, I realize the normalization procedure and some > >> statistical calculations: > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > >> RG1<- read.maimages (my.filenames, source = "agilent", columns = > >> one.col1, dec =".") > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") > >> fit1<- lmFit (MA1, design = NULL) > > > > The design matrix indicates to lmFit() what is control and what is > > treatment. When you specify a NULL design matrix, lmFit() will just use > > a vector of 1s, which would be fine if you had two-color chips and no > > dye-swaps. Otherwise, you are just testing the hypothesis that the > > average expression of all samples is not equal to zero (which obviously > > isn't correct). > > > > So if you have four conditions with four replicates (and I am assuming > > here that they are Biological replicates, not just different aliquots of > > the same sample), you want a design matrix with four columns. The > > simplest such design matrix (to me, anyway), computes the mean > > expression for each group, and then you can just make the comparisons > > you want. > > > > cond <- factor(rep(1:4, each = 4)) > > design <- model.matrix(~ 0 + cond) > > colnames(design) <- c("trt1","trt2","trt3","trt4") > > > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, > > trt4-trt2, trt4-trt3, levels = design) > > fit <- lmFit(Ma1, design) > > fit1 <- contrasts.fit(fit, contrast) > > fit1 <- eBayes(fit1) > > > > which will make all possible comparisons. > > > > Alternatively, if you just want to compare all treatments to a control > > (and assuming your control is trt1). > > > > design <- model.matrix(~cond) > > fit <- lmFit(Ma1, design) > > fit1 <- eBayes(fit) > > > > In this case, all coefficients in the model will be e.g., trt2-trt1, > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. > > > > Best, > > > > Jim > > > > > > > >> fit1<- eBay (fit1) > >> > >> But my question is: how I can specify to bioconductor which files > >> correspond to Control, or which correspond to microarrays with > >> treatment? > >> > >> Regards, Paz > >> > >> [[alternative HTML version deleted]] > >> > >> _______________________________________________ > >> Bioconductor mailing list > >> Bioconductor@r-project.org > >> https://stat.ethz.ch/mailman/listinfo/bioconductor > >> Search the archives: > >> http://news.gmane.org/gmane.science.biology.informatics.conductor > > [[alternative HTML version deleted]]
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Hi Paz, On 9/18/2011 11:20 AM, Paz Tapia Ramirez wrote: > Hi JIM. > When you say"trt1" "trt2" you mean the column GErep in the targets file ? I don't know anything about your targets file, as you didn't show us what that looks like. And as Yong noted, even though you claim a one-color array, you appear to be doing something other than a conventional one-color analysis. So first you need to clarify exactly what you are trying to do with this analysis, what kind of data these are, what your targets file looks like, etc. Best, Jim > > > Date: Fri, 16 Sep 2011 13:32:30 +0200 > > From: yong.li at zbsa.uni-freiburg.de > > To: jmacdon at med.umich.edu > > CC: verotapia at alumnos.utalca.cl; bioconductor at r-project.org > > Subject: Re: [BioC] process one color microarray > > > > Hi Paz, hi Jim, > > > > I think there is a problem here. Jim, you have given detailed > > explanation of making design matrix and do various comparisons, but they > > are based on the assumption that the input given to lmFit is log > > expression values from one color array. The problem is that, based on > > Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to > > processed signals of the green channel (of course background corrected > > and quantile normalized). I doubt very much that that piece of codes > > does what Paz wants to do. However, it's also possible that I missed > > something. > > > > Best regards, > > Yong > > > > James W. MacDonald wrote: > > > Hi Paz, > > > > > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > > >> Hello, I have a question. I'm Working with one-color microarrays . I > > >> worked in 4 conditions different and each condition I have 4 > > >> replicates. Now, my question is when I load the files to > > >> Bioconductor, I load as follows: > > >> my.filenames<- c ("Condic1_repl1.txt", > > >> "Condic1_repl2.txt", > > >> "Condic1_repl3.txt", > > >> "Condic1_repl4.txt", > > >> "Condic2_repl1.txt", > > >> "Condic2_repl2.txt", > > >> "Condic2_repl3.txt", > > >> "Condic2_repl4.txt", > > >> "Condic3_repl1.txt" > > >> "Condic3_repl2.txt", > > >> "Condic3_repl3.txt", > > >> "Condic3_repl4.txt", > > >> "Condic3_repl1.txt", > > >> "Condic3_repl2.txt", > > >> "Condic3_repl3.txt", > > >> "Condic3_repl4.txt") > > >> > > >> Subsequently, I realize the normalization procedure and some > > >> statistical calculations: > > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > > >> RG1<- read.maimages (my.filenames, source = "agilent", columns = > > >> one.col1, dec =".") > > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") > > >> fit1<- lmFit (MA1, design = NULL) > > > > > > The design matrix indicates to lmFit() what is control and what is > > > treatment. When you specify a NULL design matrix, lmFit() will just > use > > > a vector of 1s, which would be fine if you had two-color chips and no > > > dye-swaps. Otherwise, you are just testing the hypothesis that the > > > average expression of all samples is not equal to zero (which > obviously > > > isn't correct). > > > > > > So if you have four conditions with four replicates (and I am assuming > > > here that they are Biological replicates, not just different > aliquots of > > > the same sample), you want a design matrix with four columns. The > > > simplest such design matrix (to me, anyway), computes the mean > > > expression for each group, and then you can just make the comparisons > > > you want. > > > > > > cond <- factor(rep(1:4, each = 4)) > > > design <- model.matrix(~ 0 + cond) > > > colnames(design) <- c("trt1","trt2","trt3","trt4") > > > > > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, > > > trt4-trt2, trt4-trt3, levels = design) > > > fit <- lmFit(Ma1, design) > > > fit1 <- contrasts.fit(fit, contrast) > > > fit1 <- eBayes(fit1) > > > > > > which will make all possible comparisons. > > > > > > Alternatively, if you just want to compare all treatments to a control > > > (and assuming your control is trt1). > > > > > > design <- model.matrix(~cond) > > > fit <- lmFit(Ma1, design) > > > fit1 <- eBayes(fit) > > > > > > In this case, all coefficients in the model will be e.g., trt2-trt1, > > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. > > > > > > Best, > > > > > > Jim > > > > > > > > > > > >> fit1<- eBay (fit1) > > >> > > >> But my question is: how I can specify to bioconductor which files > > >> correspond to Control, or which correspond to microarrays with > > >> treatment? > > >> > > >> Regards, Paz > > >> > > >> [[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 Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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Are one- color microarrays. 4 different treatment, one of them is control. Each treatment has 4 replicas. I need to get the differential gene expression. My target file is: "FileName" "Treatment" "GErep" "ISO_1.txt" "ISO" "1" "ISO_2.txt" "ISO" "2" "ISO_3.txt" "ISO" "3" "ISO_4.txt" "ISO" "4" "LMK_2.txt" "LMK" "1" "LMK_3.txt" "LMK" "2" "LMK_4.txt" "LMK" "3" "LMK_1.txt" "LMK" "4" "T1_3.txt" "T1" "1" "T1_4.txt" "T1" "2" "T1_1.txt" "T1" "3" "T1_2.txt" "T1" "4" "T2_4.txt" "T2" "1" "T2_1.txt" "T2" "2" "T2_2.txt" "T2" "3" "T2_3.txt" "T2" "4" When you say"trt1" "trt2" you mean the column GErep in the targets file ? Help me please. Thanks > Date: Mon, 19 Sep 2011 09:06:03 -0400 > From: jmacdon@med.umich.edu > To: verotapia@alumnos.utalca.cl > CC: yong.li@zbsa.uni-freiburg.de; bioconductor@r-project.org > Subject: Re: [BioC] process one color microarray > > Hi Paz, > > On 9/18/2011 11:20 AM, Paz Tapia Ramirez wrote: > > Hi JIM. > > When you say"trt1" "trt2" you mean the column GErep in the targets file ? > > I don't know anything about your targets file, as you didn't show us > what that looks like. > > And as Yong noted, even though you claim a one-color array, you appear > to be doing something other than a conventional one-color analysis. So > first you need to clarify exactly what you are trying to do with this > analysis, what kind of data these are, what your targets file looks > like, etc. > > Best, > > Jim > > > > > > Date: Fri, 16 Sep 2011 13:32:30 +0200 > > > From: yong.li@zbsa.uni-freiburg.de > > > To: jmacdon@med.umich.edu > > > CC: verotapia@alumnos.utalca.cl; bioconductor@r-project.org > > > Subject: Re: [BioC] process one color microarray > > > > > > Hi Paz, hi Jim, > > > > > > I think there is a problem here. Jim, you have given detailed > > > explanation of making design matrix and do various comparisons, but they > > > are based on the assumption that the input given to lmFit is log > > > expression values from one color array. The problem is that, based on > > > Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to > > > processed signals of the green channel (of course background corrected > > > and quantile normalized). I doubt very much that that piece of codes > > > does what Paz wants to do. However, it's also possible that I missed > > > something. > > > > > > Best regards, > > > Yong > > > > > > James W. MacDonald wrote: > > > > Hi Paz, > > > > > > > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > > > >> Hello, I have a question. I'm Working with one-color microarrays . I > > > >> worked in 4 conditions different and each condition I have 4 > > > >> replicates. Now, my question is when I load the files to > > > >> Bioconductor, I load as follows: > > > >> my.filenames<- c ("Condic1_repl1.txt", > > > >> "Condic1_repl2.txt", > > > >> "Condic1_repl3.txt", > > > >> "Condic1_repl4.txt", > > > >> "Condic2_repl1.txt", > > > >> "Condic2_repl2.txt", > > > >> "Condic2_repl3.txt", > > > >> "Condic2_repl4.txt", > > > >> "Condic3_repl1.txt" > > > >> "Condic3_repl2.txt", > > > >> "Condic3_repl3.txt", > > > >> "Condic3_repl4.txt", > > > >> "Condic3_repl1.txt", > > > >> "Condic3_repl2.txt", > > > >> "Condic3_repl3.txt", > > > >> "Condic3_repl4.txt") > > > >> > > > >> Subsequently, I realize the normalization procedure and some > > > >> statistical calculations: > > > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > > > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > > > >> RG1<- read.maimages (my.filenames, source = "agilent", columns = > > > >> one.col1, dec =".") > > > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > > > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") > > > >> fit1<- lmFit (MA1, design = NULL) > > > > > > > > The design matrix indicates to lmFit() what is control and what is > > > > treatment. When you specify a NULL design matrix, lmFit() will just > > use > > > > a vector of 1s, which would be fine if you had two-color chips and no > > > > dye-swaps. Otherwise, you are just testing the hypothesis that the > > > > average expression of all samples is not equal to zero (which > > obviously > > > > isn't correct). > > > > > > > > So if you have four conditions with four replicates (and I am assuming > > > > here that they are Biological replicates, not just different > > aliquots of > > > > the same sample), you want a design matrix with four columns. The > > > > simplest such design matrix (to me, anyway), computes the mean > > > > expression for each group, and then you can just make the comparisons > > > > you want. > > > > > > > > cond <- factor(rep(1:4, each = 4)) > > > > design <- model.matrix(~ 0 + cond) > > > > colnames(design) <- c("trt1","trt2","trt3","trt4") > > > > > > > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, > > > > trt4-trt2, trt4-trt3, levels = design) > > > > fit <- lmFit(Ma1, design) > > > > fit1 <- contrasts.fit(fit, contrast) > > > > fit1 <- eBayes(fit1) > > > > > > > > which will make all possible comparisons. > > > > > > > > Alternatively, if you just want to compare all treatments to a control > > > > (and assuming your control is trt1). > > > > > > > > design <- model.matrix(~cond) > > > > fit <- lmFit(Ma1, design) > > > > fit1 <- eBayes(fit) > > > > > > > > In this case, all coefficients in the model will be e.g., trt2-trt1, > > > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. > > > > > > > > Best, > > > > > > > > Jim > > > > > > > > > > > > > > > >> fit1<- eBay (fit1) > > > >> > > > >> But my question is: how I can specify to bioconductor which files > > > >> correspond to Control, or which correspond to microarrays with > > > >> treatment? > > > >> > > > >> Regards, Paz > > > >> > > > >> [[alternative HTML version deleted]] > > > >> > > > >> _______________________________________________ > > > >> Bioconductor mailing list > > > >> Bioconductor@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 > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues > [[alternative HTML version deleted]]
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Hello, when I create a design of a matrix with: cond <- factor (rep (1:4, EACH = 2)) # 2 replicates of each treatment design <- model.matrix (~ 0 + cond) colname (design) <- c ("treatment1", "treatment2", "treatment3", "treatment4") contrast <- makeContrasts (treatment2-treatment1, treatment3-treatment1, treatment4-treatment1, Levels = design) How bioconductor knows the treatment1, treatment2, treatment3 and treatment4? Files must be loaded in any particular order? Thanks, Paz [[alternative HTML version deleted]]
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Jim, I need to do an analysis of expression differential of genes. I do not know if using M values ​​or log-ratios. Regards, > Date: Mon, 19 Sep 2011 09:06:03 -0400 > From: jmacdon@med.umich.edu > To: verotapia@alumnos.utalca.cl > CC: yong.li@zbsa.uni-freiburg.de; bioconductor@r-project.org > Subject: Re: [BioC] process one color microarray > > Hi Paz, > > On 9/18/2011 11:20 AM, Paz Tapia Ramirez wrote: > > Hi JIM. > > When you say"trt1" "trt2" you mean the column GErep in the targets file ? > > I don't know anything about your targets file, as you didn't show us > what that looks like. > > And as Yong noted, even though you claim a one-color array, you appear > to be doing something other than a conventional one-color analysis. So > first you need to clarify exactly what you are trying to do with this > analysis, what kind of data these are, what your targets file looks > like, etc. > > Best, > > Jim > > > > > > Date: Fri, 16 Sep 2011 13:32:30 +0200 > > > From: yong.li@zbsa.uni-freiburg.de > > > To: jmacdon@med.umich.edu > > > CC: verotapia@alumnos.utalca.cl; bioconductor@r-project.org > > > Subject: Re: [BioC] process one color microarray > > > > > > Hi Paz, hi Jim, > > > > > > I think there is a problem here. Jim, you have given detailed > > > explanation of making design matrix and do various comparisons, but they > > > are based on the assumption that the input given to lmFit is log > > > expression values from one color array. The problem is that, based on > > > Paz's code, in the MAList MA1 the M values are log 2 ratios of mean to > > > processed signals of the green channel (of course background corrected > > > and quantile normalized). I doubt very much that that piece of codes > > > does what Paz wants to do. However, it's also possible that I missed > > > something. > > > > > > Best regards, > > > Yong > > > > > > James W. MacDonald wrote: > > > > Hi Paz, > > > > > > > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > > > >> Hello, I have a question. I'm Working with one-color microarrays . I > > > >> worked in 4 conditions different and each condition I have 4 > > > >> replicates. Now, my question is when I load the files to > > > >> Bioconductor, I load as follows: > > > >> my.filenames<- c ("Condic1_repl1.txt", > > > >> "Condic1_repl2.txt", > > > >> "Condic1_repl3.txt", > > > >> "Condic1_repl4.txt", > > > >> "Condic2_repl1.txt", > > > >> "Condic2_repl2.txt", > > > >> "Condic2_repl3.txt", > > > >> "Condic2_repl4.txt", > > > >> "Condic3_repl1.txt" > > > >> "Condic3_repl2.txt", > > > >> "Condic3_repl3.txt", > > > >> "Condic3_repl4.txt", > > > >> "Condic3_repl1.txt", > > > >> "Condic3_repl2.txt", > > > >> "Condic3_repl3.txt", > > > >> "Condic3_repl4.txt") > > > >> > > > >> Subsequently, I realize the normalization procedure and some > > > >> statistical calculations: > > > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > > > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > > > >> RG1<- read.maimages (my.filenames, source = "agilent", columns = > > > >> one.col1, dec =".") > > > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > > > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") > > > >> fit1<- lmFit (MA1, design = NULL) > > > > > > > > The design matrix indicates to lmFit() what is control and what is > > > > treatment. When you specify a NULL design matrix, lmFit() will just > > use > > > > a vector of 1s, which would be fine if you had two-color chips and no > > > > dye-swaps. Otherwise, you are just testing the hypothesis that the > > > > average expression of all samples is not equal to zero (which > > obviously > > > > isn't correct). > > > > > > > > So if you have four conditions with four replicates (and I am assuming > > > > here that they are Biological replicates, not just different > > aliquots of > > > > the same sample), you want a design matrix with four columns. The > > > > simplest such design matrix (to me, anyway), computes the mean > > > > expression for each group, and then you can just make the comparisons > > > > you want. > > > > > > > > cond <- factor(rep(1:4, each = 4)) > > > > design <- model.matrix(~ 0 + cond) > > > > colnames(design) <- c("trt1","trt2","trt3","trt4") > > > > > > > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, trt3-trt2, > > > > trt4-trt2, trt4-trt3, levels = design) > > > > fit <- lmFit(Ma1, design) > > > > fit1 <- contrasts.fit(fit, contrast) > > > > fit1 <- eBayes(fit1) > > > > > > > > which will make all possible comparisons. > > > > > > > > Alternatively, if you just want to compare all treatments to a control > > > > (and assuming your control is trt1). > > > > > > > > design <- model.matrix(~cond) > > > > fit <- lmFit(Ma1, design) > > > > fit1 <- eBayes(fit) > > > > > > > > In this case, all coefficients in the model will be e.g., trt2-trt1, > > > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts directly. > > > > > > > > Best, > > > > > > > > Jim > > > > > > > > > > > > > > > >> fit1<- eBay (fit1) > > > >> > > > >> But my question is: how I can specify to bioconductor which files > > > >> correspond to Control, or which correspond to microarrays with > > > >> treatment? > > > >> > > > >> Regards, Paz > > > >> > > > >> [[alternative HTML version deleted]] > > > >> > > > >> _______________________________________________ > > > >> Bioconductor mailing list > > > >> Bioconductor@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 > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues > [[alternative HTML version deleted]]
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Hi Paz, You seem not to know at all what you are doing, and I don't know if some hints here and there on a list will be sufficient to get you started. My primary recommendation for you is to find a local statistician who can help you do this analysis. This would be the best course of action for you to take. If that isn't possible, you need to read the limma User's Guide thoroughly. In addition, there are several monographs (the Bioconductor book being a great example) that you could also read. You cannot rely on the kindness of others to help you get this analysis done - if you insist on doing it yourself, you will have to take some initiative of your own and learn what you need to know. Best, Jim On 9/21/11 4:48 PM, Paz Tapia Ramirez wrote: > Jim, > I need to do an analysis of expression differential of genes. I do not > know if using M values ??or log-ratios. > > Regards, > > > > Date: Mon, 19 Sep 2011 09:06:03 -0400 > > From: jmacdon at med.umich.edu > > To: verotapia at alumnos.utalca.cl > > CC: yong.li at zbsa.uni-freiburg.de; bioconductor at r-project.org > > Subject: Re: [BioC] process one color microarray > > > > Hi Paz, > > > > On 9/18/2011 11:20 AM, Paz Tapia Ramirez wrote: > > > Hi JIM. > > > When you say"trt1" "trt2" you mean the column GErep in the targets > file ? > > > > I don't know anything about your targets file, as you didn't show us > > what that looks like. > > > > And as Yong noted, even though you claim a one-color array, you appear > > to be doing something other than a conventional one-color analysis. So > > first you need to clarify exactly what you are trying to do with this > > analysis, what kind of data these are, what your targets file looks > > like, etc. > > > > Best, > > > > Jim > > > > > > > > > Date: Fri, 16 Sep 2011 13:32:30 +0200 > > > > From: yong.li at zbsa.uni-freiburg.de > > > > To: jmacdon at med.umich.edu > > > > CC: verotapia at alumnos.utalca.cl; bioconductor at r-project.org > > > > Subject: Re: [BioC] process one color microarray > > > > > > > > Hi Paz, hi Jim, > > > > > > > > I think there is a problem here. Jim, you have given detailed > > > > explanation of making design matrix and do various comparisons, > but they > > > > are based on the assumption that the input given to lmFit is log > > > > expression values from one color array. The problem is that, > based on > > > > Paz's code, in the MAList MA1 the M values are log 2 ratios of > mean to > > > > processed signals of the green channel (of course background > corrected > > > > and quantile normalized). I doubt very much that that piece of codes > > > > does what Paz wants to do. However, it's also possible that I missed > > > > something. > > > > > > > > Best regards, > > > > Yong > > > > > > > > James W. MacDonald wrote: > > > > > Hi Paz, > > > > > > > > > > On 9/14/2011 5:55 PM, Paz Tapia Ramirez wrote: > > > > >> Hello, I have a question. I'm Working with one-color > microarrays . I > > > > >> worked in 4 conditions different and each condition I have 4 > > > > >> replicates. Now, my question is when I load the files to > > > > >> Bioconductor, I load as follows: > > > > >> my.filenames<- c ("Condic1_repl1.txt", > > > > >> "Condic1_repl2.txt", > > > > >> "Condic1_repl3.txt", > > > > >> "Condic1_repl4.txt", > > > > >> "Condic2_repl1.txt", > > > > >> "Condic2_repl2.txt", > > > > >> "Condic2_repl3.txt", > > > > >> "Condic2_repl4.txt", > > > > >> "Condic3_repl1.txt" > > > > >> "Condic3_repl2.txt", > > > > >> "Condic3_repl3.txt", > > > > >> "Condic3_repl4.txt", > > > > >> "Condic3_repl1.txt", > > > > >> "Condic3_repl2.txt", > > > > >> "Condic3_repl3.txt", > > > > >> "Condic3_repl4.txt") > > > > >> > > > > >> Subsequently, I realize the normalization procedure and some > > > > >> statistical calculations: > > > > >> one.col1<-list (R = "gMeanSignal" G = "gProcessedSignal" > > > > >> Rb = "gBGMedianSignal", Gb = "gProcessedBackground") > > > > >> RG1<- read.maimages (my.filenames, source = "agilent", columns = > > > > >> one.col1, dec =".") > > > > >> RG1<- backgroundCorrect (RG1, method = "half", offset = 50) > > > > >> MA1<- normalizeBetweenArrays (RG1, method = "quantile") > > > > >> fit1<- lmFit (MA1, design = NULL) > > > > > > > > > > The design matrix indicates to lmFit() what is control and what is > > > > > treatment. When you specify a NULL design matrix, lmFit() will > just > > > use > > > > > a vector of 1s, which would be fine if you had two-color chips > and no > > > > > dye-swaps. Otherwise, you are just testing the hypothesis that the > > > > > average expression of all samples is not equal to zero (which > > > obviously > > > > > isn't correct). > > > > > > > > > > So if you have four conditions with four replicates (and I am > assuming > > > > > here that they are Biological replicates, not just different > > > aliquots of > > > > > the same sample), you want a design matrix with four columns. The > > > > > simplest such design matrix (to me, anyway), computes the mean > > > > > expression for each group, and then you can just make the > comparisons > > > > > you want. > > > > > > > > > > cond <- factor(rep(1:4, each = 4)) > > > > > design <- model.matrix(~ 0 + cond) > > > > > colnames(design) <- c("trt1","trt2","trt3","trt4") > > > > > > > > > > contrast <- makeContrasts(trt2-trt1, trt3-trt1, trt4-trt1, > trt3-trt2, > > > > > trt4-trt2, trt4-trt3, levels = design) > > > > > fit <- lmFit(Ma1, design) > > > > > fit1 <- contrasts.fit(fit, contrast) > > > > > fit1 <- eBayes(fit1) > > > > > > > > > > which will make all possible comparisons. > > > > > > > > > > Alternatively, if you just want to compare all treatments to a > control > > > > > (and assuming your control is trt1). > > > > > > > > > > design <- model.matrix(~cond) > > > > > fit <- lmFit(Ma1, design) > > > > > fit1 <- eBayes(fit) > > > > > > > > > > In this case, all coefficients in the model will be e.g., > trt2-trt1, > > > > > trt3-trt1, trt4-trt1, so you don't have to specify contrasts > directly. > > > > > > > > > > Best, > > > > > > > > > > Jim > > > > > > > > > > > > > > > > > > > >> fit1<- eBay (fit1) > > > > >> > > > > >> But my question is: how I can specify to bioconductor which files > > > > >> correspond to Control, or which correspond to microarrays with > > > > >> treatment? > > > > >> > > > > >> Regards, Paz > > > > >> > > > > >> [[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 > > Douglas Lab > > University of Michigan > > Department of Human Genetics > > 5912 Buhl > > 1241 E. Catherine St. > > Ann Arbor MI 48109-5618 > > 734-615-7826 > > ********************************************************** > > Electronic Mail is not secure, may not be read every day, and should > not be used for urgent or sensitive issues > > -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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