LIMMAing normalized and background corrected MA-data derived by a text file
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@glaer-christine-4699
Last seen 10.2 years ago
Dear all, I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: MA <- read.maimages(targets, columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? Best regards, and thank you, Christine Gl??er ---------------------------------------------------------------------- - Christine Gl??er Institute of Bioinformatics and Systems Biology Helmholtz Zentrum M?nchen Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) Ingolst?dter Landstr. 1 85764 Neuherberg www.helmholtz-muenchen.de Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum Registergericht: Amtsgericht M?nchen HRB 6466 USt-IdNr: DE 129521671
Microarray Normalization probe Microarray Normalization probe • 1.7k views
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Yong Li ▴ 190
@yong-li-3321
Last seen 10.2 years ago
Dear Christine, the function read.maimages gives you a RGList. To convert RGList to MAList, the functions normalizeBetweenArrays can be used. You can use method="none" when calling the function to omitting any normalizations. For more details type help(normalizeBetweenArrays) in your R session. Best regards, Yong Gl??er, Christine wrote: > Dear all, > > I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: > > MA <- read.maimages(targets, columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) > > Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? > > Best regards, and thank you, > > > Christine Gl??er > > > -------------------------------------------------------------------- --- > Christine Gl??er > Institute of Bioinformatics and Systems Biology > > Helmholtz Zentrum M?nchen > Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) > Ingolst?dter Landstr. 1 > 85764 Neuherberg > www.helmholtz-muenchen.de > Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe > Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum > Registergericht: Amtsgericht M?nchen HRB 6466 > USt-IdNr: DE 129521671 > > _______________________________________________ > 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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Dear Yong, thank you very much for your response. I proceeded like you suggested: RG_ma <- normalizeBetweenArrays(RG, method="none") then averaged the replica (which are not spotted regularly) E.avg <- avereps(test, ID=RG_ma$genes) and fitted the values design <- modelMatrix(targets, ref="control") fit <- lmFit(E.avg, design) fit2 <- eBayes(fit) However, no gene is significantly differentially expressed (adj.p-value 0.99xxx for each gene, BH), which cannot be true (compared to results calculated with CybRT (http://cybert.ics.uci.edu/help/index.html); some genes missing would be reasonable, but all genes being not significantly diff. expressed?). Do you have any suggestions what is going wrong? Best wishes, Christine ---------------------------------------------------------------------- - Christine Gl??er Institut f?r Bioinformatik und Systembiologie Tel.: +49-(0)89/31873583 ________________________________________ Von: Yong Li [yong.li at zbsa.uni-freiburg.de] Gesendet: Freitag, 17. Juni 2011 11:28 An: Gl??er, Christine Cc: bioconductor at r-project.org Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file Dear Christine, the function read.maimages gives you a RGList. To convert RGList to MAList, the functions normalizeBetweenArrays can be used. You can use method="none" when calling the function to omitting any normalizations. For more details type help(normalizeBetweenArrays) in your R session. Best regards, Yong Gl??er, Christine wrote: > Dear all, > > I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: > > MA <- read.maimages(targets, columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) > > Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? > > Best regards, and thank you, > > > Christine Gl??er > > > -------------------------------------------------------------------- --- > Christine Gl??er > Institute of Bioinformatics and Systems Biology > > Helmholtz Zentrum M?nchen > Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) > Ingolst?dter Landstr. 1 > 85764 Neuherberg > www.helmholtz-muenchen.de > Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe > Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum > Registergericht: Amtsgericht M?nchen HRB 6466 > USt-IdNr: DE 129521671 > > _______________________________________________ > 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 > Helmholtz Zentrum M?nchen Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) Ingolst?dter Landstr. 1 85764 Neuherberg www.helmholtz-muenchen.de Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum Registergericht: Amtsgericht M?nchen HRB 6466 USt-IdNr: DE 129521671
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On Fri, Jun 17, 2011 at 6:06 AM, "Gl??er, Christine" <christine.glaesser at="" helmholtz-muenchen.de=""> wrote: > Dear Yong, > > thank you very much for your response. I proceeded like you suggested: > > RG_ma <- normalizeBetweenArrays(RG, method="none") > > then averaged the replica (which are not spotted regularly) > > E.avg <- avereps(test, ID=RG_ma$genes) > > and fitted the values > > design <- modelMatrix(targets, ref="control") > fit <- lmFit(E.avg, design) > fit2 <- eBayes(fit) > > However, no gene is significantly differentially expressed (adj.p-value 0.99xxx for each gene, BH), which cannot be true (compared to results calculated with CybRT (http://cybert.ics.uci.edu/help/index.html); some genes missing would be reasonable, but all genes being not significantly diff. expressed?). Do you have any suggestions what is going wrong? > We do not know what "targets" or "design" above look like. Perhaps you could share those? Sean > -------------------------------------------------------------------- --- > Christine Gl??er > Institut f?r Bioinformatik und Systembiologie > Tel.: +49-(0)89/31873583 > ________________________________________ > Von: Yong Li [yong.li at zbsa.uni-freiburg.de] > Gesendet: Freitag, 17. Juni 2011 11:28 > An: Gl??er, Christine > Cc: bioconductor at r-project.org > Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file > > Dear Christine, > > the function read.maimages gives you a RGList. To convert RGList to > MAList, the functions normalizeBetweenArrays can be used. You can use > method="none" when calling the function to omitting any normalizations. > For more details type help(normalizeBetweenArrays) in your R session. > > Best regards, > Yong > > Gl??er, Christine wrote: >> Dear all, >> >> I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: >> >> MA <- read.maimages(targets, ?columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) >> >> Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? >> >> Best regards, and thank you, >> >> >> Christine Gl??er >> >> >> ------------------------------------------------------------------- ---- >> Christine Gl??er >> Institute of Bioinformatics and Systems Biology >> >> Helmholtz Zentrum M?nchen >> Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) >> Ingolst?dter Landstr. 1 >> 85764 Neuherberg >> www.helmholtz-muenchen.de >> Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe >> Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum >> Registergericht: Amtsgericht M?nchen HRB 6466 >> USt-IdNr: DE 129521671 >> >> _______________________________________________ >> 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 >> > > Helmholtz Zentrum M?nchen > Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) > Ingolst?dter Landstr. 1 > 85764 Neuherberg > www.helmholtz-muenchen.de > Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe > Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum > Registergericht: Amtsgericht M?nchen HRB 6466 > USt-IdNr: DE 129521671 > > _______________________________________________ > 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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Dear Sean, sorry for that. Targets is my targets file: SlideNumber Name FileName Cy3 Cy5 1 c1 exp_214.txt mutant control 2 c2 exp_217.txt control mutant 3 c3 exp_220.txt mutant control design: design <- modelMatrix(targets, ref="control") My script in total: library(limma) tfile <- "Targets.txt" outfile <- "/firstrun.txt" targets <- readTargets(tfile) RG <- read.maimages(targets, columns=list(G="Sucrose",R="Control"), annotation=c("Block","Row","Column","Name","ID")) ###I've added block=1, row=1 and column consecutively numbered for each array, to be able to use getLayout for E.avg (see below) RG$printer <- getLayout(RG$genes) RG_ma <- normalizeBetweenArrays(RG, method="none") E.avg <- avereps(test, ID=RG_ma$genes) #### if I don't use getLayout or block/row/column like described above, doubled gene names will be deleted, but uncoupled from the values... leaving e.g. 5 gene names and 10 values, as short example design <- modelMatrix(targets, ref="control") fit <- lmFit(E.avg, design) fit2 <- eBayes(fit) sink(outfile, options(max.print=5.5E5)) print ("Mutant-Control") print (topTable(fit2, coef=1, number=50000, adjust="BH")) sink() Thank you :) ---------------------------------------------------------------------- - Christine Gl??er Institut f?r Bioinformatik und Systembiologie Tel.: +49-(0)89/31873583 ________________________________________ Von: seandavi at gmail.com [seandavi at gmail.com] im Auftrag von Sean Davis [sdavis2 at mail.nih.gov] Gesendet: Freitag, 17. Juni 2011 12:25 An: Gl??er, Christine Cc: Yong Li; bioconductor at r-project.org Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file On Fri, Jun 17, 2011 at 6:06 AM, "Gl??er, Christine" <christine.glaesser at="" helmholtz-muenchen.de=""> wrote: > Dear Yong, > > thank you very much for your response. I proceeded like you suggested: > > RG_ma <- normalizeBetweenArrays(RG, method="none") > > then averaged the replica (which are not spotted regularly) > > E.avg <- avereps(test, ID=RG_ma$genes) > > and fitted the values > > design <- modelMatrix(targets, ref="control") > fit <- lmFit(E.avg, design) > fit2 <- eBayes(fit) > > However, no gene is significantly differentially expressed (adj.p-value 0.99xxx for each gene, BH), which cannot be true (compared to results calculated with CybRT (http://cybert.ics.uci.edu/help/index.html); some genes missing would be reasonable, but all genes being not significantly diff. expressed?). Do you have any suggestions what is going wrong? > We do not know what "targets" or "design" above look like. Perhaps you could share those? Sean > -------------------------------------------------------------------- --- > Christine Gl??er > Institut f?r Bioinformatik und Systembiologie > Tel.: +49-(0)89/31873583 > ________________________________________ > Von: Yong Li [yong.li at zbsa.uni-freiburg.de] > Gesendet: Freitag, 17. Juni 2011 11:28 > An: Gl??er, Christine > Cc: bioconductor at r-project.org > Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file > > Dear Christine, > > the function read.maimages gives you a RGList. To convert RGList to > MAList, the functions normalizeBetweenArrays can be used. You can use > method="none" when calling the function to omitting any normalizations. > For more details type help(normalizeBetweenArrays) in your R session. > > Best regards, > Yong > > Gl??er, Christine wrote: >> Dear all, >> >> I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: >> >> MA <- read.maimages(targets, columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) >> >> Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? >> >> Best regards, and thank you, >> >> >> Christine Gl??er >> >> >> ------------------------------------------------------------------- ---- >> Christine Gl??er >> Institute of Bioinformatics and Systems Biology >> >> Helmholtz Zentrum M?nchen >> Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) >> Ingolst?dter Landstr. 1 >> 85764 Neuherberg >> www.helmholtz-muenchen.de >> Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe >> Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum >> Registergericht: Amtsgericht M?nchen HRB 6466 >> USt-IdNr: DE 129521671 >> >> _______________________________________________ >> 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 >> > > Helmholtz Zentrum M?nchen > Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) > Ingolst?dter Landstr. 1 > 85764 Neuherberg > www.helmholtz-muenchen.de > Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe > Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum > Registergericht: Amtsgericht M?nchen HRB 6466 > USt-IdNr: DE 129521671 > > _______________________________________________ > 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 > Helmholtz Zentrum M?nchen Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) Ingolst?dter Landstr. 1 85764 Neuherberg www.helmholtz-muenchen.de Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum Registergericht: Amtsgericht M?nchen HRB 6466 USt-IdNr: DE 129521671
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Sorry, I misspelled the script in the last mail. My script, corrected: library(limma) tfile <- "Targets.txt" outfile <- "firstrun.txt" targets <- readTargets(tfile) RG <- read.maimages(targets, columns=list(G="mutant", R="control"), annotation=c("Block","Row","Column","Name","ID")) RG$printer <- getLayout(RG$genes) RG_ma <- normalizeBetweenArrays(RG, method="none") E.avg <- avereps(test, ID=RG_ma$genes) design <- modelMatrix(targets, ref="control") fit <- lmFit(E.avg, design) fit2 <- eBayes(fit) sink(outfile, options(max.print=5.5E5)) print ("Mutant-Control") print (topTable(fit2, coef=1, number=50000, adjust="BH")) sink() with targets: SlideNumber Name FileName Cy3 Cy5 1 c1 exp_214.txt mutant control 2 c2 exp_217.txt control mutant 3 c3 exp_220.txt mutant control sorry for the inconvenience. ---------------------------------------------------------------------- - Christine Gl??er Institut f?r Bioinformatik und Systembiologie Tel.: +49-(0)89/31873583 ________________________________________ Von: seandavi at gmail.com [seandavi at gmail.com] im Auftrag von Sean Davis [sdavis2 at mail.nih.gov] Gesendet: Freitag, 17. Juni 2011 12:25 An: Gl??er, Christine Cc: Yong Li; bioconductor at r-project.org Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file On Fri, Jun 17, 2011 at 6:06 AM, "Gl??er, Christine" <christine.glaesser at="" helmholtz-muenchen.de=""> wrote: > Dear Yong, > > thank you very much for your response. I proceeded like you suggested: > > RG_ma <- normalizeBetweenArrays(RG, method="none") > > then averaged the replica (which are not spotted regularly) > > E.avg <- avereps(test, ID=RG_ma$genes) > > and fitted the values > > design <- modelMatrix(targets, ref="control") > fit <- lmFit(E.avg, design) > fit2 <- eBayes(fit) > > However, no gene is significantly differentially expressed (adj.p-value 0.99xxx for each gene, BH), which cannot be true (compared to results calculated with CybRT (http://cybert.ics.uci.edu/help/index.html); some genes missing would be reasonable, but all genes being not significantly diff. expressed?). Do you have any suggestions what is going wrong? > We do not know what "targets" or "design" above look like. Perhaps you could share those? Sean > -------------------------------------------------------------------- --- > Christine Gl??er > Institut f?r Bioinformatik und Systembiologie > Tel.: +49-(0)89/31873583 > ________________________________________ > Von: Yong Li [yong.li at zbsa.uni-freiburg.de] > Gesendet: Freitag, 17. Juni 2011 11:28 > An: Gl??er, Christine > Cc: bioconductor at r-project.org > Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file > > Dear Christine, > > the function read.maimages gives you a RGList. To convert RGList to > MAList, the functions normalizeBetweenArrays can be used. You can use > method="none" when calling the function to omitting any normalizations. > For more details type help(normalizeBetweenArrays) in your R session. > > Best regards, > Yong > > Gl??er, Christine wrote: >> Dear all, >> >> I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: >> >> MA <- read.maimages(targets, columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) >> >> Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? >> >> Best regards, and thank you, >> >> >> Christine Gl??er >> >> >> ------------------------------------------------------------------- ---- >> Christine Gl??er >> Institute of Bioinformatics and Systems Biology >> >> Helmholtz Zentrum M?nchen >> Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) >> Ingolst?dter Landstr. 1 >> 85764 Neuherberg >> www.helmholtz-muenchen.de >> Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe >> Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum >> Registergericht: Amtsgericht M?nchen HRB 6466 >> USt-IdNr: DE 129521671 >> >> _______________________________________________ >> 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 >> > > Helmholtz Zentrum M?nchen > Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) > Ingolst?dter Landstr. 1 > 85764 Neuherberg > www.helmholtz-muenchen.de > Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe > Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum > Registergericht: Amtsgericht M?nchen HRB 6466 > USt-IdNr: DE 129521671 > > _______________________________________________ > 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 > Helmholtz Zentrum M?nchen Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) Ingolst?dter Landstr. 1 85764 Neuherberg www.helmholtz-muenchen.de Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum Registergericht: Amtsgericht M?nchen HRB 6466 USt-IdNr: DE 129521671
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Dear mailing-list, I just want to inform you that the problem is solved. It was a simple input error; I swapped a dye-swap back without realizing it in a first, preparatory formatting step, leading to wrong targets. Actually, on one array the values of the red signal were not, as the targets stated, derived by the mutant line, but the control, which lead to highly unsignificant p-values. So, I used the script as previously described: library(limma) tfile <- "Targets.txt" outfile <- "firstrun.txt" targets <- readTargets(tfile) RG <- read.maimages(targets, columns=list(G="green",R="red"), annotation=c("Block","Row","Column","Name","ID")) RG$printer <- getLayout(RG$genes) ### optional at last RG_ma <- normalizeBetweenArrays(RG, method="none") ### as previously suggested; converting the input into a MAList for subsequent manipulation without any corrections E.avg <- avereps(RG_ma, ID=RG_ma$genes$ID) design <- modelMatrix(targets, ref="control") fit <- lmFit(E.avg, design) fit2 <- eBayes(fit) sink(outfile, options(max.print=5.5E5)) print ("Mutant-Control") print (topTable(fit2, coef=1, number=50000, adjust="BH")) sink() using previously formatted input files containing block, row and column. Thanks, and best regards, Christine Gl??er ---------------------------------------------------------------------- - Christine Gl??er Institut f?r Bioinformatik und Systembiologie Tel.: +49-(0)89/31873583 ________________________________________ Von: seandavi at gmail.com [seandavi at gmail.com] im Auftrag von Sean Davis [sdavis2 at mail.nih.gov] Gesendet: Freitag, 17. Juni 2011 12:25 An: Gl??er, Christine Cc: Yong Li; bioconductor at r-project.org Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file On Fri, Jun 17, 2011 at 6:06 AM, "Gl??er, Christine" <christine.glaesser at="" helmholtz-muenchen.de=""> wrote: > Dear Yong, > > thank you very much for your response. I proceeded like you suggested: > > RG_ma <- normalizeBetweenArrays(RG, method="none") > > then averaged the replica (which are not spotted regularly) > > E.avg <- avereps(test, ID=RG_ma$genes) > > and fitted the values > > design <- modelMatrix(targets, ref="control") > fit <- lmFit(E.avg, design) > fit2 <- eBayes(fit) > > However, no gene is significantly differentially expressed (adj.p-value 0.99xxx for each gene, BH), which cannot be true (compared to results calculated with CybRT (http://cybert.ics.uci.edu/help/index.html); some genes missing would be reasonable, but all genes being not significantly diff. expressed?). Do you have any suggestions what is going wrong? > We do not know what "targets" or "design" above look like. Perhaps you could share those? Sean > -------------------------------------------------------------------- --- > Christine Gl??er > Institut f?r Bioinformatik und Systembiologie > Tel.: +49-(0)89/31873583 > ________________________________________ > Von: Yong Li [yong.li at zbsa.uni-freiburg.de] > Gesendet: Freitag, 17. Juni 2011 11:28 > An: Gl??er, Christine > Cc: bioconductor at r-project.org > Betreff: Re: [BioC] LIMMAing normalized and background corrected MA- data derived by a text file > > Dear Christine, > > the function read.maimages gives you a RGList. To convert RGList to > MAList, the functions normalizeBetweenArrays can be used. You can use > method="none" when calling the function to omitting any normalizations. > For more details type help(normalizeBetweenArrays) in your R session. > > Best regards, > Yong > > Gl??er, Christine wrote: >> Dear all, >> >> I have two-color microarray data, which was given to me after normalization (lowess) and background correction in a text file. Thus, the data looks like: probe ID - Gene name - red signal - green signal, no background information is left. I use read.maimages for reading the data in: >> >> MA <- read.maimages(targets, columns=list(G="mutant",R="control"), annotation=c("Name", "ID")) >> >> Subsequently, I'd like to analyze these data ommitting the normalization and background correction, since it is already normalized and background corrected. However, lmFit only accepts MALists (and others, just as example here), and I'm not sure how to convert the data appropriate. How should I set the M-value and the A-value, for example? Is it even possible to analyze those data ommitting normalization and background correction and directly start with lmFit and subsequent steps? Or did someone else encounter a similar problem and could tell me her/his way of dealing with these data? >> >> Best regards, and thank you, >> >> >> Christine Gl??er >> >> >> ------------------------------------------------------------------- ---- >> Christine Gl??er >> Institute of Bioinformatics and Systems Biology >> >> Helmholtz Zentrum M?nchen >> Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) >> Ingolst?dter Landstr. 1 >> 85764 Neuherberg >> www.helmholtz-muenchen.de >> Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe >> Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum >> Registergericht: Amtsgericht M?nchen HRB 6466 >> USt-IdNr: DE 129521671 >> >> _______________________________________________ >> 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 >> > > Helmholtz Zentrum M?nchen > Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) > Ingolst?dter Landstr. 1 > 85764 Neuherberg > www.helmholtz-muenchen.de > Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe > Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum > Registergericht: Amtsgericht M?nchen HRB 6466 > USt-IdNr: DE 129521671 > > _______________________________________________ > 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 > Helmholtz Zentrum M?nchen Deutsches Forschungszentrum f?r Gesundheit und Umwelt (GmbH) Ingolst?dter Landstr. 1 85764 Neuherberg www.helmholtz-muenchen.de Aufsichtsratsvorsitzende: MinDir?in B?rbel Brumme-Bothe Gesch?ftsf?hrer: Prof. Dr. G?nther Wess und Dr. Nikolaus Blum Registergericht: Amtsgericht M?nchen HRB 6466 USt-IdNr: DE 129521671
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