AgiMicroRna - FilterMicroRna question
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Neel Aluru ▴ 460
@neel-aluru-3760
Last seen 7.3 years ago
United States
Hello, I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. > ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS FILTERING BY ControlType Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], ncol = dim(ddFILT)[2]) : attempt to set an attribute on NULL I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. Thank you very much, Neel Session Info > library("AgiMicroRna") > targets.micro=readTargets(infile="targets.txt", verbose=TRUE) Target File FileName Treatment GErep Subject 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > dd.micro=read.maimages(targets.micro$FileName, columns=list(R="gTotalGeneSignal",G= "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), annotation=c("ProbeUID","ControlType","ProbeName","GeneName","Systemat icName", "sequence", "accessions","probe_mappings", "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt > cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) Foreground: MeanSignal FILTERING BY ControlType FLAG RAW DATA: 5335 PROBES without CONTROLS: 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490 (Non-CTRL) Unique Genes: 231 ---------------------------------- DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20 18 36 416 ------------------------------------------------------ Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 0.078 0.081 0.091 0.081 0.077 0.067 0.076 0.066 0.103 60_TCDD_1 60_TCDD_2 60_TCDD_3 0.073 0.086 0.069 ------------------------------------------------------ DISTRIBUTION OF REPLICATED Noncontrol Genes reps 20 231 ------------------------------------------------------ > ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) Calculating Expression > ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS FILTERING BY ControlType Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], ncol = dim(ddFILT)[2]) : attempt to set an attribute on NULL > MMM = ddTGS.rma$Rb > colnames(MMM) = colnames(dd.micro$Rb) > maintitle='TGS.rma' > colorfill='blue' > ddaux=ddTGS.rma > ddaux$G=MMM > mvaMicroRna(ddaux, maintitle, verbose=TRUE) ------------------------------------------------------ mvaMicroRna info: FEATURES : 231 POSITIVE CTRL: 12 NEGATIVE CTRL: 7 STRUCTURAL: 3 > rm(ddaux) > RleMicroRna(MMM,"RLE TGS.rma", colorfill) > boxplotMicroRna(MMM, maintitle, colorfill) > plotDensityMicroRna(MMM, maintitle) > spottypes = readSpotTypes() > ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) Matching patterns for: ProbeName GeneName Found 231 gene Found 1 BLANK Found 1 Blank Found 0 blank Found 6 positive Found 0 negative Found 0 flag1 Found 0 flag2 Found 6 flag3 Found 5 flag4 Found 1 flag5 Setting attributes: values > i = ddTGS.rma$genes$Status == "gene" > esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, makePLOT=TRUE, verbose = TRUE) outPUT DATA: esetPROC Features Samples 231 12 > design=model.matrix(~-1+treatment) > print(design) treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD 1 1 0 0 0 2 1 0 0 0 3 1 0 0 0 4 0 1 0 0 5 0 1 0 0 6 0 1 0 0 7 0 0 1 0 8 0 0 1 0 9 0 0 1 0 10 0 0 0 1 11 0 0 0 1 12 0 0 0 1 attr(,"assign") [1] 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$treatment [1] "contr.treatment" > fit=lmFit(esetPROC, design) > cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = treatment60DMSO-treatment36DMSO, levels=design) > print(cont.matrix) Contrasts Levels treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO treatment36DMSO -1 0 treatment36TCDD 1 0 treatment60DMSO 0 -1 treatment60TCDD 0 1 Contrasts Levels treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO treatment36DMSO 0 -1 treatment36TCDD -1 0 treatment60DMSO 0 1 treatment60TCDD 1 0 > fit2 = contrasts.fit(fit,cont.matrix) > print(head(fit2$coeff)) Contrasts treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO dre-let-7a 0.038640984 0.013333873 dre-let-7b 0.074038749 -0.031608286 dre-let-7c 0.026244357 -0.005682488 dre-let-7d 0.067340768 0.055567054 dre-let-7e 0.004569306 0.136348664 dre-let-7f 0.042880109 0.085568058 Contrasts treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO dre-let-7a 1.7358343 1.76114142 dre-let-7b 0.1366920 0.24233899 dre-let-7c 0.9920976 1.02402449 dre-let-7d 0.8098432 0.82161694 dre-let-7e 0.1186829 -0.01309647 dre-let-7f 1.1245878 1.08189990 > fit2 = eBayes(fit2) > fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) DATA Features Samples 231 12 > DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = "BH", PVcut = 0.1, verbose = TRUE) ------------------------------------------------------ Method for Selecting DEGs: separate Multiple Testing method: BH - pval 0.1 treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO UP 0 5 DOWN 0 1 treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO UP 56 51 DOWN 80 91 ------------------------------------------------------ > pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", MTestmethod="BH",cont.matrix, verbose= TRUE) > significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = 0.1, Mcut=0, verbose=TRUE) ------------------------------------------------------ CONTRAST: 1 - treatment36TCDDvstreatment36DMSO Error in data.frame(PROBE_ID, as.character(GENE_ID), as.character(chr_coord), : arguments imply differing number of rows: 231, 0 Neel Aluru Postdoctoral Scholar Biology Department Woods Hole Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607
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@martin-morgan-1513
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On 06/01/2010 06:43 AM, Neel Aluru wrote: > Hello, > > I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. > >> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, > IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, > limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) > FILTERING PROBES BY FLAGS > > > FILTERING BY ControlType > Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], > ncol = dim(ddFILT)[2]) : > attempt to set an attribute on NULL > > > I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. Hi Neel -- I can't help with specifics, but > matrix(NULL) Error in matrix(NULL) : attempt to set an attribute on NULL so the proximate cause of the error message is likely that ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't exist. You can investigate this by inspecting the code, e.g., > options(error=browser()) and then re-running your code. See ?browser; when done use options(error=NULL). Before that I'd revisit the help page for this function and double-check that you are providing appropriate arguments. I've added > packageDescription('AgiMicroRna')$Maintainer [1] "Pedro Lopez-Romero <plopez at="" cnic.es="">" to the email, as Pedro in the best position to help you. Martin > Thank you very much, > > Neel > > > > > Session Info > >> library("AgiMicroRna") >> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) > > Target File > FileName Treatment GErep Subject > 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 > 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 > 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 > 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 > 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 > 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 > 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 > 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 > 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 > 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 > 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 > 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > >> dd.micro=read.maimages(targets.micro$FileName, > columns=list(R="gTotalGeneSignal",G= > "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), > annotation=c("ProbeUID","ControlType","ProbeName","GeneName","System aticName", > "sequence", "accessions","probe_mappings", > "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", > "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) > Read 36_DMSO_1.txt > Read 36_DMSO_2.txt > Read 36_DMSO_3.txt > Read 36_TCDD_1.txt > Read 36_TCDD_2.txt > Read 36_TCDD_3.txt > Read 60_DMSO_1.txt > Read 60_DMSO_2.txt > Read 60_DMSO_3.txt > Read 60_TCDD_1.txt > Read 60_TCDD_2.txt > Read 60_TCDD_3.txt >> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) > Foreground: MeanSignal > > FILTERING BY ControlType FLAG > > RAW DATA: 5335 > PROBES without CONTROLS: 4620 > ---------------------------------- > (Non-CTRL) Unique Probe: 490 > (Non-CTRL) Unique Genes: 231 > ---------------------------------- > DISTRIBUTION OF REPLICATED NonControl Probes > reps > 5 6 7 10 > 20 18 36 416 > ------------------------------------------------------ > Replication at Probe level- MEDIAN CV > 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 > 60_DMSO_2 60_DMSO_3 > 0.078 0.081 0.091 0.081 0.077 0.067 > 0.076 0.066 0.103 > 60_TCDD_1 60_TCDD_2 60_TCDD_3 > 0.073 0.086 0.069 > ------------------------------------------------------ > DISTRIBUTION OF REPLICATED Noncontrol Genes > reps > 20 > 231 > ------------------------------------------------------ >> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) > Calculating Expression >> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, > IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, > limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) > FILTERING PROBES BY FLAGS > > > FILTERING BY ControlType > Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], > ncol = dim(ddFILT)[2]) : > attempt to set an attribute on NULL > >> MMM = ddTGS.rma$Rb >> colnames(MMM) = colnames(dd.micro$Rb) >> maintitle='TGS.rma' >> colorfill='blue' >> ddaux=ddTGS.rma >> ddaux$G=MMM >> mvaMicroRna(ddaux, maintitle, verbose=TRUE) > > ------------------------------------------------------ > mvaMicroRna info: > FEATURES : 231 > POSITIVE CTRL: 12 > NEGATIVE CTRL: 7 > STRUCTURAL: 3 >> rm(ddaux) >> RleMicroRna(MMM,"RLE TGS.rma", colorfill) >> boxplotMicroRna(MMM, maintitle, colorfill) >> plotDensityMicroRna(MMM, maintitle) >> spottypes = readSpotTypes() >> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) > Matching patterns for: ProbeName GeneName > Found 231 gene > Found 1 BLANK > Found 1 Blank > Found 0 blank > Found 6 positive > Found 0 negative > Found 0 flag1 > Found 0 flag2 > Found 6 flag3 > Found 5 flag4 > Found 1 flag5 > Setting attributes: values >> i = ddTGS.rma$genes$Status == "gene" >> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, > makePLOT=TRUE, verbose = TRUE) > outPUT DATA: esetPROC > Features Samples > 231 12 >> design=model.matrix(~-1+treatment) >> print(design) > treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD > 1 1 0 0 0 > 2 1 0 0 0 > 3 1 0 0 0 > 4 0 1 0 0 > 5 0 1 0 0 > 6 0 1 0 0 > 7 0 0 1 0 > 8 0 0 1 0 > 9 0 0 1 0 > 10 0 0 0 1 > 11 0 0 0 1 > 12 0 0 0 1 > attr(,"assign") > [1] 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$treatment > [1] "contr.treatment" > >> fit=lmFit(esetPROC, design) >> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = > treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = > treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = > treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = > treatment60DMSO-treatment36DMSO, levels=design) >> print(cont.matrix) > Contrasts > Levels treatment36TCDDvstreatment36DMSO > treatment60TCDDvstreatment60DMSO > treatment36DMSO -1 > 0 > treatment36TCDD 1 > 0 > treatment60DMSO 0 > -1 > treatment60TCDD 0 > 1 > Contrasts > Levels treatment60TCDDvstreatment36TCDD > treatment60DMSOvstreatment36DMSO > treatment36DMSO 0 > -1 > treatment36TCDD -1 > 0 > treatment60DMSO 0 > 1 > treatment60TCDD 1 > 0 >> fit2 = contrasts.fit(fit,cont.matrix) >> print(head(fit2$coeff)) > Contrasts > treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO > dre-let-7a 0.038640984 0.013333873 > dre-let-7b 0.074038749 -0.031608286 > dre-let-7c 0.026244357 -0.005682488 > dre-let-7d 0.067340768 0.055567054 > dre-let-7e 0.004569306 0.136348664 > dre-let-7f 0.042880109 0.085568058 > Contrasts > treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO > dre-let-7a 1.7358343 1.76114142 > dre-let-7b 0.1366920 0.24233899 > dre-let-7c 0.9920976 1.02402449 > dre-let-7d 0.8098432 0.82161694 > dre-let-7e 0.1186829 -0.01309647 > dre-let-7f 1.1245878 1.08189990 >> fit2 = eBayes(fit2) >> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) > DATA > Features Samples > 231 12 > >> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = > "BH", PVcut = 0.1, verbose = TRUE) > > ------------------------------------------------------ > Method for Selecting DEGs: separate > Multiple Testing method: BH - pval 0.1 > > treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO > UP 0 5 > DOWN 0 1 > treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO > UP 56 51 > DOWN 80 91 > ------------------------------------------------------ >> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", > MTestmethod="BH",cont.matrix, verbose= TRUE) >> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, > cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = > 0.1, Mcut=0, verbose=TRUE) > ------------------------------------------------------ > CONTRAST: 1 - treatment36TCDDvstreatment36DMSO > > Error in data.frame(PROBE_ID, as.character(GENE_ID), > as.character(chr_coord), : > arguments imply differing number of rows: 231, 0 > > > > > Neel Aluru > Postdoctoral Scholar > Biology Department > Woods Hole Oceanographic Institution > Woods Hole, MA 02543 > USA > 508-289-3607 > > _______________________________________________ > 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 -- Martin Morgan Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793
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Thanks, Martin. I have contacted Pedro today and hopefully he will get a chance to see my mail. In the mean time I will follow your suggestions. Thanks once again. Neel On Jun 1, 2010, at 1:31 PM, Martin Morgan wrote: > On 06/01/2010 06:43 AM, Neel Aluru wrote: >> Hello, >> >> I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. >> >>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >> FILTERING PROBES BY FLAGS >> >> >> FILTERING BY ControlType >> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >> ncol = dim(ddFILT)[2]) : >> attempt to set an attribute on NULL >> >> >> I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. > > Hi Neel -- I can't help with specifics, but > >> matrix(NULL) > Error in matrix(NULL) : attempt to set an attribute on NULL > > so the proximate cause of the error message is likely that > ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't > exist. You can investigate this by inspecting the code, e.g., > >> options(error=browser()) > > and then re-running your code. See ?browser; when done use > options(error=NULL). Before that I'd revisit the help page for this > function and double-check that you are providing appropriate arguments. > > I've added > >> packageDescription('AgiMicroRna')$Maintainer > [1] "Pedro Lopez-Romero <plopez at="" cnic.es="">" > > to the email, as Pedro in the best position to help you. > > Martin > >> Thank you very much, >> >> Neel >> >> >> >> >> Session Info >> >>> library("AgiMicroRna") >>> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) >> >> Target File >> FileName Treatment GErep Subject >> 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 >> 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 >> 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 >> 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 >> 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 >> 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 >> 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 >> 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 >> 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 >> 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 >> 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 >> 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 >> >>> dd.micro=read.maimages(targets.micro$FileName, >> columns=list(R="gTotalGeneSignal",G= >> "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), >> annotation=c("ProbeUID","ControlType","ProbeName","GeneName","Syste maticName", >> "sequence", "accessions","probe_mappings", >> "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", >> "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) >> Read 36_DMSO_1.txt >> Read 36_DMSO_2.txt >> Read 36_DMSO_3.txt >> Read 36_TCDD_1.txt >> Read 36_TCDD_2.txt >> Read 36_TCDD_3.txt >> Read 60_DMSO_1.txt >> Read 60_DMSO_2.txt >> Read 60_DMSO_3.txt >> Read 60_TCDD_1.txt >> Read 60_TCDD_2.txt >> Read 60_TCDD_3.txt >>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) >> Foreground: MeanSignal >> >> FILTERING BY ControlType FLAG >> >> RAW DATA: 5335 >> PROBES without CONTROLS: 4620 >> ---------------------------------- >> (Non-CTRL) Unique Probe: 490 >> (Non-CTRL) Unique Genes: 231 >> ---------------------------------- >> DISTRIBUTION OF REPLICATED NonControl Probes >> reps >> 5 6 7 10 >> 20 18 36 416 >> ------------------------------------------------------ >> Replication at Probe level- MEDIAN CV >> 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 >> 60_DMSO_2 60_DMSO_3 >> 0.078 0.081 0.091 0.081 0.077 0.067 >> 0.076 0.066 0.103 >> 60_TCDD_1 60_TCDD_2 60_TCDD_3 >> 0.073 0.086 0.069 >> ------------------------------------------------------ >> DISTRIBUTION OF REPLICATED Noncontrol Genes >> reps >> 20 >> 231 >> ------------------------------------------------------ >>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >> Calculating Expression >>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >> FILTERING PROBES BY FLAGS >> >> >> FILTERING BY ControlType >> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >> ncol = dim(ddFILT)[2]) : >> attempt to set an attribute on NULL >> >>> MMM = ddTGS.rma$Rb >>> colnames(MMM) = colnames(dd.micro$Rb) >>> maintitle='TGS.rma' >>> colorfill='blue' >>> ddaux=ddTGS.rma >>> ddaux$G=MMM >>> mvaMicroRna(ddaux, maintitle, verbose=TRUE) >> >> ------------------------------------------------------ >> mvaMicroRna info: >> FEATURES : 231 >> POSITIVE CTRL: 12 >> NEGATIVE CTRL: 7 >> STRUCTURAL: 3 >>> rm(ddaux) >>> RleMicroRna(MMM,"RLE TGS.rma", colorfill) >>> boxplotMicroRna(MMM, maintitle, colorfill) >>> plotDensityMicroRna(MMM, maintitle) >>> spottypes = readSpotTypes() >>> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) >> Matching patterns for: ProbeName GeneName >> Found 231 gene >> Found 1 BLANK >> Found 1 Blank >> Found 0 blank >> Found 6 positive >> Found 0 negative >> Found 0 flag1 >> Found 0 flag2 >> Found 6 flag3 >> Found 5 flag4 >> Found 1 flag5 >> Setting attributes: values >>> i = ddTGS.rma$genes$Status == "gene" >>> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, >> makePLOT=TRUE, verbose = TRUE) >> outPUT DATA: esetPROC >> Features Samples >> 231 12 >>> design=model.matrix(~-1+treatment) >>> print(design) >> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD >> 1 1 0 0 0 >> 2 1 0 0 0 >> 3 1 0 0 0 >> 4 0 1 0 0 >> 5 0 1 0 0 >> 6 0 1 0 0 >> 7 0 0 1 0 >> 8 0 0 1 0 >> 9 0 0 1 0 >> 10 0 0 0 1 >> 11 0 0 0 1 >> 12 0 0 0 1 >> attr(,"assign") >> [1] 1 1 1 1 >> attr(,"contrasts") >> attr(,"contrasts")$treatment >> [1] "contr.treatment" >> >>> fit=lmFit(esetPROC, design) >>> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = >> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = >> treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = >> treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = >> treatment60DMSO-treatment36DMSO, levels=design) >>> print(cont.matrix) >> Contrasts >> Levels treatment36TCDDvstreatment36DMSO >> treatment60TCDDvstreatment60DMSO >> treatment36DMSO -1 >> 0 >> treatment36TCDD 1 >> 0 >> treatment60DMSO 0 >> -1 >> treatment60TCDD 0 >> 1 >> Contrasts >> Levels treatment60TCDDvstreatment36TCDD >> treatment60DMSOvstreatment36DMSO >> treatment36DMSO 0 >> -1 >> treatment36TCDD -1 >> 0 >> treatment60DMSO 0 >> 1 >> treatment60TCDD 1 >> 0 >>> fit2 = contrasts.fit(fit,cont.matrix) >>> print(head(fit2$coeff)) >> Contrasts >> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >> dre-let-7a 0.038640984 0.013333873 >> dre-let-7b 0.074038749 -0.031608286 >> dre-let-7c 0.026244357 -0.005682488 >> dre-let-7d 0.067340768 0.055567054 >> dre-let-7e 0.004569306 0.136348664 >> dre-let-7f 0.042880109 0.085568058 >> Contrasts >> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >> dre-let-7a 1.7358343 1.76114142 >> dre-let-7b 0.1366920 0.24233899 >> dre-let-7c 0.9920976 1.02402449 >> dre-let-7d 0.8098432 0.82161694 >> dre-let-7e 0.1186829 -0.01309647 >> dre-let-7f 1.1245878 1.08189990 >>> fit2 = eBayes(fit2) >>> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) >> DATA >> Features Samples >> 231 12 >> >>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = >> "BH", PVcut = 0.1, verbose = TRUE) >> >> ------------------------------------------------------ >> Method for Selecting DEGs: separate >> Multiple Testing method: BH - pval 0.1 >> >> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >> UP 0 5 >> DOWN 0 1 >> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >> UP 56 51 >> DOWN 80 91 >> ------------------------------------------------------ >>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", >> MTestmethod="BH",cont.matrix, verbose= TRUE) >>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, >> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = >> 0.1, Mcut=0, verbose=TRUE) >> ------------------------------------------------------ >> CONTRAST: 1 - treatment36TCDDvstreatment36DMSO >> >> Error in data.frame(PROBE_ID, as.character(GENE_ID), >> as.character(chr_coord), : >> arguments imply differing number of rows: 231, 0 >> >> >> >> >> Neel Aluru >> Postdoctoral Scholar >> Biology Department >> Woods Hole Oceanographic Institution >> Woods Hole, MA 02543 >> USA >> 508-289-3607 >> >> _______________________________________________ >> 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 > > > -- > Martin Morgan > Computational Biology / Fred Hutchinson Cancer Research Center > 1100 Fairview Ave. N. > PO Box 19024 Seattle, WA 98109 > > Location: Arnold Building M1 B861 > Phone: (206) 667-2793 > Neel Aluru Postdoctoral Scholar Biology Department Woods Hole Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607
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Hi Neel, Try to use readMicroRnaAFE(targets,verbose=TRUE) to load your data into R instead of calling read.maimages() by yourself. This will solve your problem Cheers p.- -----Mensaje original----- De: Neel Aluru [mailto:naluru at whoi.edu] Enviado el: Tuesday, June 01, 2010 7:34 PM Para: Martin Morgan CC: bioc; Pedro L?pez Romero Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question Thanks, Martin. I have contacted Pedro today and hopefully he will get a chance to see my mail. In the mean time I will follow your suggestions. Thanks once again. Neel On Jun 1, 2010, at 1:31 PM, Martin Morgan wrote: > On 06/01/2010 06:43 AM, Neel Aluru wrote: >> Hello, >> >> I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. >> >>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >> FILTERING PROBES BY FLAGS >> >> >> FILTERING BY ControlType >> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >> ncol = dim(ddFILT)[2]) : >> attempt to set an attribute on NULL >> >> >> I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. > > Hi Neel -- I can't help with specifics, but > >> matrix(NULL) > Error in matrix(NULL) : attempt to set an attribute on NULL > > so the proximate cause of the error message is likely that > ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't > exist. You can investigate this by inspecting the code, e.g., > >> options(error=browser()) > > and then re-running your code. See ?browser; when done use > options(error=NULL). Before that I'd revisit the help page for this > function and double-check that you are providing appropriate arguments. > > I've added > >> packageDescription('AgiMicroRna')$Maintainer > [1] "Pedro Lopez-Romero <plopez at="" cnic.es="">" > > to the email, as Pedro in the best position to help you. > > Martin > >> Thank you very much, >> >> Neel >> >> >> >> >> Session Info >> >>> library("AgiMicroRna") >>> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) >> >> Target File >> FileName Treatment GErep Subject >> 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 >> 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 >> 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 >> 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 >> 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 >> 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 >> 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 >> 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 >> 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 >> 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 >> 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 >> 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 >> >>> dd.micro=read.maimages(targets.micro$FileName, >> columns=list(R="gTotalGeneSignal",G= >> "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), >> annotation=c("ProbeUID","ControlType","ProbeName","GeneName","Syste maticName", >> "sequence", "accessions","probe_mappings", >> "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", >> "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) >> Read 36_DMSO_1.txt >> Read 36_DMSO_2.txt >> Read 36_DMSO_3.txt >> Read 36_TCDD_1.txt >> Read 36_TCDD_2.txt >> Read 36_TCDD_3.txt >> Read 60_DMSO_1.txt >> Read 60_DMSO_2.txt >> Read 60_DMSO_3.txt >> Read 60_TCDD_1.txt >> Read 60_TCDD_2.txt >> Read 60_TCDD_3.txt >>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) >> Foreground: MeanSignal >> >> FILTERING BY ControlType FLAG >> >> RAW DATA: 5335 >> PROBES without CONTROLS: 4620 >> ---------------------------------- >> (Non-CTRL) Unique Probe: 490 >> (Non-CTRL) Unique Genes: 231 >> ---------------------------------- >> DISTRIBUTION OF REPLICATED NonControl Probes >> reps >> 5 6 7 10 >> 20 18 36 416 >> ------------------------------------------------------ >> Replication at Probe level- MEDIAN CV >> 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 >> 60_DMSO_2 60_DMSO_3 >> 0.078 0.081 0.091 0.081 0.077 0.067 >> 0.076 0.066 0.103 >> 60_TCDD_1 60_TCDD_2 60_TCDD_3 >> 0.073 0.086 0.069 >> ------------------------------------------------------ >> DISTRIBUTION OF REPLICATED Noncontrol Genes >> reps >> 20 >> 231 >> ------------------------------------------------------ >>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >> Calculating Expression >>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >> FILTERING PROBES BY FLAGS >> >> >> FILTERING BY ControlType >> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >> ncol = dim(ddFILT)[2]) : >> attempt to set an attribute on NULL >> >>> MMM = ddTGS.rma$Rb >>> colnames(MMM) = colnames(dd.micro$Rb) >>> maintitle='TGS.rma' >>> colorfill='blue' >>> ddaux=ddTGS.rma >>> ddaux$G=MMM >>> mvaMicroRna(ddaux, maintitle, verbose=TRUE) >> >> ------------------------------------------------------ >> mvaMicroRna info: >> FEATURES : 231 >> POSITIVE CTRL: 12 >> NEGATIVE CTRL: 7 >> STRUCTURAL: 3 >>> rm(ddaux) >>> RleMicroRna(MMM,"RLE TGS.rma", colorfill) >>> boxplotMicroRna(MMM, maintitle, colorfill) >>> plotDensityMicroRna(MMM, maintitle) >>> spottypes = readSpotTypes() >>> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) >> Matching patterns for: ProbeName GeneName >> Found 231 gene >> Found 1 BLANK >> Found 1 Blank >> Found 0 blank >> Found 6 positive >> Found 0 negative >> Found 0 flag1 >> Found 0 flag2 >> Found 6 flag3 >> Found 5 flag4 >> Found 1 flag5 >> Setting attributes: values >>> i = ddTGS.rma$genes$Status == "gene" >>> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, >> makePLOT=TRUE, verbose = TRUE) >> outPUT DATA: esetPROC >> Features Samples >> 231 12 >>> design=model.matrix(~-1+treatment) >>> print(design) >> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD >> 1 1 0 0 0 >> 2 1 0 0 0 >> 3 1 0 0 0 >> 4 0 1 0 0 >> 5 0 1 0 0 >> 6 0 1 0 0 >> 7 0 0 1 0 >> 8 0 0 1 0 >> 9 0 0 1 0 >> 10 0 0 0 1 >> 11 0 0 0 1 >> 12 0 0 0 1 >> attr(,"assign") >> [1] 1 1 1 1 >> attr(,"contrasts") >> attr(,"contrasts")$treatment >> [1] "contr.treatment" >> >>> fit=lmFit(esetPROC, design) >>> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = >> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = >> treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = >> treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = >> treatment60DMSO-treatment36DMSO, levels=design) >>> print(cont.matrix) >> Contrasts >> Levels treatment36TCDDvstreatment36DMSO >> treatment60TCDDvstreatment60DMSO >> treatment36DMSO -1 >> 0 >> treatment36TCDD 1 >> 0 >> treatment60DMSO 0 >> -1 >> treatment60TCDD 0 >> 1 >> Contrasts >> Levels treatment60TCDDvstreatment36TCDD >> treatment60DMSOvstreatment36DMSO >> treatment36DMSO 0 >> -1 >> treatment36TCDD -1 >> 0 >> treatment60DMSO 0 >> 1 >> treatment60TCDD 1 >> 0 >>> fit2 = contrasts.fit(fit,cont.matrix) >>> print(head(fit2$coeff)) >> Contrasts >> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >> dre-let-7a 0.038640984 0.013333873 >> dre-let-7b 0.074038749 -0.031608286 >> dre-let-7c 0.026244357 -0.005682488 >> dre-let-7d 0.067340768 0.055567054 >> dre-let-7e 0.004569306 0.136348664 >> dre-let-7f 0.042880109 0.085568058 >> Contrasts >> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >> dre-let-7a 1.7358343 1.76114142 >> dre-let-7b 0.1366920 0.24233899 >> dre-let-7c 0.9920976 1.02402449 >> dre-let-7d 0.8098432 0.82161694 >> dre-let-7e 0.1186829 -0.01309647 >> dre-let-7f 1.1245878 1.08189990 >>> fit2 = eBayes(fit2) >>> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) >> DATA >> Features Samples >> 231 12 >> >>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = >> "BH", PVcut = 0.1, verbose = TRUE) >> >> ------------------------------------------------------ >> Method for Selecting DEGs: separate >> Multiple Testing method: BH - pval 0.1 >> >> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >> UP 0 5 >> DOWN 0 1 >> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >> UP 56 51 >> DOWN 80 91 >> ------------------------------------------------------ >>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", >> MTestmethod="BH",cont.matrix, verbose= TRUE) >>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, >> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = >> 0.1, Mcut=0, verbose=TRUE) >> ------------------------------------------------------ >> CONTRAST: 1 - treatment36TCDDvstreatment36DMSO >> >> Error in data.frame(PROBE_ID, as.character(GENE_ID), >> as.character(chr_coord), : >> arguments imply differing number of rows: 231, 0 >> >> >> >> >> Neel Aluru >> Postdoctoral Scholar >> Biology Department >> Woods Hole Oceanographic Institution >> Woods Hole, MA 02543 >> USA >> 508-289-3607 >> >> _______________________________________________ >> 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 > > > -- > Martin Morgan > Computational Biology / Fred Hutchinson Cancer Research Center > 1100 Fairview Ave. N. > PO Box 19024 Seattle, WA 98109 > > Location: Arnold Building M1 B861 > Phone: (206) 667-2793 > Neel Aluru Postdoctoral Scholar Biology Department Woods Hole Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607 *************** AVISO LEGAL *************** Este mensaje va 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Hi Pedro, I tried following your suggestions and I still an error message as, "FILTERING BY ControlType Error in data.frame(as.character(PROBE_ID), as.character(GENE_ID), as.character(probe.chr), : arguments imply differing number of rows: 231, 0". I looked into the mailing list archive and I saw one post there with similar issue but they were trying to modify the source code. I am not an expert in R and do not want to play with source code. If you have any suggestions on where the problem lies with the above error message, that will be great. Sorry to bother you on this. Thank you very much for your help. Sincerely, Neel The following is the session info. > library("AgiMicroRna") > targets.micro=readTargets(infile="targets.txt", verbose=TRUE) Target File FileName Treatment GErep Subject 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > ddaux=read.maimages(files=targets.micro$FileName,source="agilent", + + other.columns=list(IsGeneDetected="gIsGeneDetected", + + IsSaturated="gIsSaturated", + + IsFeatNonUnifOF="gIsFeatNonUnifOL", + + IsFeatPopnOL="gIsFeatPopnOL", + + ChrCoord="chr_coord", + + BGKmd="gBGMedianSignal", + + BGKus="gBGUsed"), + + columns=list(Rf="gTotalGeneSignal", + + Gf="gTotalProbeSignal", + + Rb="gMeanSignal", + + Gb="gProcessedSignal"), + + verbose=TRUE,sep="\t",quote="") Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt > names(ddaux) [1] "R" "G" "Rb" "Gb" "targets" "genes" "source" "other" > names(ddaux$genes) [1] "Row" "Col" "Start" "Sequence" "ProbeUID" "ControlType" [7] "ProbeName" "GeneName" "SystematicName" "Description" > dd=readMicroRnaAFE(targets.micro, verbose=TRUE) Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt RGList: dd$R: 'gTotalGeneSignal' dd$G: 'gTotalProbeSignal' dd$Rb: 'gMeanSignal' dd$Gb: 'gProcessedSignal' > dd$genes=ddaux$genes[,c(6,7,8)] > cvArray(dd, "MeanSignal", targets.micro, verbose=TRUE) Foreground: MeanSignal FILTERING BY ControlType FLAG RAW DATA: 5335 PROBES without CONTROLS: 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490 (Non-CTRL) Unique Genes: 231 ---------------------------------- DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20 18 36 416 ------------------------------------------------------ Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 60_TCDD_1 60_TCDD_2 0.078 0.081 0.091 0.081 0.077 0.067 0.076 0.066 0.103 0.073 0.086 60_TCDD_3 0.069 ------------------------------------------------------ DISTRIBUTION OF REPLICATED Noncontrol Genes reps 20 231 ------------------------------------------------------ > ddTGS.rma = rmaMicroRna(dd, normalize=TRUE, background=FALSE) Calculating Expression > ddPROC = filterMicroRna(ddTGS.rma, dd, control = TRUE, IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS FILTERING BY ControlType Error in data.frame(as.character(PROBE_ID), as.character(GENE_ID), as.character(probe.chr), : arguments imply differing number of rows: 231, 0 On Jun 2, 2010, at 5:32 AM, Pedro L?pez Romero wrote: > Hi Neel, > Try to use readMicroRnaAFE(targets,verbose=TRUE) to load your data into R instead of calling read.maimages() by yourself. This will solve your problem > > Cheers > > p.- > > > -----Mensaje original----- > De: Neel Aluru [mailto:naluru at whoi.edu] > Enviado el: Tuesday, June 01, 2010 7:34 PM > Para: Martin Morgan > CC: bioc; Pedro L?pez Romero > Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question > > Thanks, Martin. I have contacted Pedro today and hopefully he will get a chance to see my mail. In the mean time I will follow your suggestions. > > Thanks once again. > > Neel > > On Jun 1, 2010, at 1:31 PM, Martin Morgan wrote: > >> On 06/01/2010 06:43 AM, Neel Aluru wrote: >>> Hello, >>> >>> I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. > >>> >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >>> FILTERING PROBES BY FLAGS >>> >>> >>> FILTERING BY ControlType >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >>> ncol = dim(ddFILT)[2]) : >>> attempt to set an attribute on NULL >>> >>> >>> I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. >> >> Hi Neel -- I can't help with specifics, but >> >>> matrix(NULL) >> Error in matrix(NULL) : attempt to set an attribute on NULL >> >> so the proximate cause of the error message is likely that >> ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't >> exist. You can investigate this by inspecting the code, e.g., >> >>> options(error=browser()) >> >> and then re-running your code. See ?browser; when done use >> options(error=NULL). Before that I'd revisit the help page for this >> function and double-check that you are providing appropriate arguments. >> >> I've added >> >>> packageDescription('AgiMicroRna')$Maintainer >> [1] "Pedro Lopez-Romero <plopez at="" cnic.es="">" >> >> to the email, as Pedro in the best position to help you. >> >> Martin >> >>> Thank you very much, >>> >>> Neel >>> >>> >>> >>> >>> Session Info >>> >>>> library("AgiMicroRna") >>>> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) >>> >>> Target File >>> FileName Treatment GErep Subject >>> 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 >>> 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 >>> 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 >>> 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 >>> 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 >>> 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 >>> 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 >>> 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 >>> 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 >>> 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 >>> 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 >>> 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 >>> >>>> dd.micro=read.maimages(targets.micro$FileName, >>> columns=list(R="gTotalGeneSignal",G= >>> "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), >>> annotation=c("ProbeUID","ControlType","ProbeName","GeneName","Syst ematicName", >>> "sequence", "accessions","probe_mappings", >>> "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", >>> "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) >>> Read 36_DMSO_1.txt >>> Read 36_DMSO_2.txt >>> Read 36_DMSO_3.txt >>> Read 36_TCDD_1.txt >>> Read 36_TCDD_2.txt >>> Read 36_TCDD_3.txt >>> Read 60_DMSO_1.txt >>> Read 60_DMSO_2.txt >>> Read 60_DMSO_3.txt >>> Read 60_TCDD_1.txt >>> Read 60_TCDD_2.txt >>> Read 60_TCDD_3.txt >>>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) >>> Foreground: MeanSignal >>> >>> FILTERING BY ControlType FLAG >>> >>> RAW DATA: 5335 >>> PROBES without CONTROLS: 4620 >>> ---------------------------------- >>> (Non-CTRL) Unique Probe: 490 >>> (Non-CTRL) Unique Genes: 231 >>> ---------------------------------- >>> DISTRIBUTION OF REPLICATED NonControl Probes >>> reps >>> 5 6 7 10 >>> 20 18 36 416 >>> ------------------------------------------------------ >>> Replication at Probe level- MEDIAN CV >>> 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 >>> 60_DMSO_2 60_DMSO_3 >>> 0.078 0.081 0.091 0.081 0.077 0.067 >>> 0.076 0.066 0.103 >>> 60_TCDD_1 60_TCDD_2 60_TCDD_3 >>> 0.073 0.086 0.069 >>> ------------------------------------------------------ >>> DISTRIBUTION OF REPLICATED Noncontrol Genes >>> reps >>> 20 >>> 231 >>> ------------------------------------------------------ >>>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >>> Calculating Expression >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >>> FILTERING PROBES BY FLAGS >>> >>> >>> FILTERING BY ControlType >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >>> ncol = dim(ddFILT)[2]) : >>> attempt to set an attribute on NULL >>> >>>> MMM = ddTGS.rma$Rb >>>> colnames(MMM) = colnames(dd.micro$Rb) >>>> maintitle='TGS.rma' >>>> colorfill='blue' >>>> ddaux=ddTGS.rma >>>> ddaux$G=MMM >>>> mvaMicroRna(ddaux, maintitle, verbose=TRUE) >>> >>> ------------------------------------------------------ >>> mvaMicroRna info: >>> FEATURES : 231 >>> POSITIVE CTRL: 12 >>> NEGATIVE CTRL: 7 >>> STRUCTURAL: 3 >>>> rm(ddaux) >>>> RleMicroRna(MMM,"RLE TGS.rma", colorfill) >>>> boxplotMicroRna(MMM, maintitle, colorfill) >>>> plotDensityMicroRna(MMM, maintitle) >>>> spottypes = readSpotTypes() >>>> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) >>> Matching patterns for: ProbeName GeneName >>> Found 231 gene >>> Found 1 BLANK >>> Found 1 Blank >>> Found 0 blank >>> Found 6 positive >>> Found 0 negative >>> Found 0 flag1 >>> Found 0 flag2 >>> Found 6 flag3 >>> Found 5 flag4 >>> Found 1 flag5 >>> Setting attributes: values >>>> i = ddTGS.rma$genes$Status == "gene" >>>> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, >>> makePLOT=TRUE, verbose = TRUE) >>> outPUT DATA: esetPROC >>> Features Samples >>> 231 12 >>>> design=model.matrix(~-1+treatment) >>>> print(design) >>> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD >>> 1 1 0 0 0 >>> 2 1 0 0 0 >>> 3 1 0 0 0 >>> 4 0 1 0 0 >>> 5 0 1 0 0 >>> 6 0 1 0 0 >>> 7 0 0 1 0 >>> 8 0 0 1 0 >>> 9 0 0 1 0 >>> 10 0 0 0 1 >>> 11 0 0 0 1 >>> 12 0 0 0 1 >>> attr(,"assign") >>> [1] 1 1 1 1 >>> attr(,"contrasts") >>> attr(,"contrasts")$treatment >>> [1] "contr.treatment" >>> >>>> fit=lmFit(esetPROC, design) >>>> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = >>> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = >>> treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = >>> treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = >>> treatment60DMSO-treatment36DMSO, levels=design) >>>> print(cont.matrix) >>> Contrasts >>> Levels treatment36TCDDvstreatment36DMSO >>> treatment60TCDDvstreatment60DMSO >>> treatment36DMSO -1 >>> 0 >>> treatment36TCDD 1 >>> 0 >>> treatment60DMSO 0 >>> -1 >>> treatment60TCDD 0 >>> 1 >>> Contrasts >>> Levels treatment60TCDDvstreatment36TCDD >>> treatment60DMSOvstreatment36DMSO >>> treatment36DMSO 0 >>> -1 >>> treatment36TCDD -1 >>> 0 >>> treatment60DMSO 0 >>> 1 >>> treatment60TCDD 1 >>> 0 >>>> fit2 = contrasts.fit(fit,cont.matrix) >>>> print(head(fit2$coeff)) >>> Contrasts >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >>> dre-let-7a 0.038640984 0.013333873 >>> dre-let-7b 0.074038749 -0.031608286 >>> dre-let-7c 0.026244357 -0.005682488 >>> dre-let-7d 0.067340768 0.055567054 >>> dre-let-7e 0.004569306 0.136348664 >>> dre-let-7f 0.042880109 0.085568058 >>> Contrasts >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >>> dre-let-7a 1.7358343 1.76114142 >>> dre-let-7b 0.1366920 0.24233899 >>> dre-let-7c 0.9920976 1.02402449 >>> dre-let-7d 0.8098432 0.82161694 >>> dre-let-7e 0.1186829 -0.01309647 >>> dre-let-7f 1.1245878 1.08189990 >>>> fit2 = eBayes(fit2) >>>> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) >>> DATA >>> Features Samples >>> 231 12 >>> >>>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = >>> "BH", PVcut = 0.1, verbose = TRUE) >>> >>> ------------------------------------------------------ >>> Method for Selecting DEGs: separate >>> Multiple Testing method: BH - pval 0.1 >>> >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >>> UP 0 5 >>> DOWN 0 1 >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >>> UP 56 51 >>> DOWN 80 91 >>> ------------------------------------------------------ >>>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", >>> MTestmethod="BH",cont.matrix, verbose= TRUE) >>>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, >>> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = >>> 0.1, Mcut=0, verbose=TRUE) >>> ------------------------------------------------------ >>> CONTRAST: 1 - treatment36TCDDvstreatment36DMSO >>> >>> Error in data.frame(PROBE_ID, as.character(GENE_ID), >>> as.character(chr_coord), : >>> arguments imply differing number of rows: 231, 0 >>> >>> >>> >>> >>> Neel Aluru >>> Postdoctoral Scholar >>> Biology Department >>> Woods Hole Oceanographic Institution >>> Woods Hole, MA 02543 >>> USA >>> 508-289-3607 >>> >>> _______________________________________________ >>> 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 >> >> >> -- >> Martin Morgan >> Computational Biology / Fred Hutchinson Cancer Research Center >> 1100 Fairview Ave. N. >> PO Box 19024 Seattle, WA 98109 >> >> Location: Arnold Building M1 B861 >> Phone: (206) 667-2793 >> > > Neel Aluru > Postdoctoral Scholar > Biology Department > Woods Hole Oceanographic Institution > Woods Hole, MA 02543 > USA > 508-289-3607 > > > > > *************** AVISO LEGAL *************** > Este mensaje va dirigido, de manera exclusiva, a su destinatario y > contiene informaci?n confidencial y sujeta al secreto profesional, > cuya divulgaci?n no est? permitida por la ley. 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Si no > consintiese la utilizaci?n del correo electr?nico o de las > comunicaciones v?a Internet le rogamos nos lo comunique y ponga en > nuestro conocimiento de manera inmediata. > > *************** LEGAL NOTICE ************** > This message is intended exclusively for the person to whom it is > addressed and contains privileged and confidential information > protected from disclosure by law. If you are not the addressee > indicated in this message, you should immediately delete it and any > attachments and notify the sender by reply e-mail or by phone > (+34 914531200). In such case, you are hereby notified that any > dissemination, distribution, copying or use of this message or any > attachments, for any purpose, is strictly prohibited by law. We > hereby inform you, as addressee of this message, that e-mail and > Internet do not guarantee the confidentiality, nor the completeness > or proper reception of the messages sent and, thus, CNIC does not > assume any liability for those circumstances. Should you not agree > to the use of e-mail or to communications via Internet, you are > kindly requested to notify us immediately. > Neel Aluru Postdoctoral Scholar Biology Department Woods Hole Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607
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Hi, I don?t understand why you are using ddaux=read.maimages and then dd=readMicroRnaAFE(targets.micro, verbose=TRUE). If readMicroRnaAFE is giving you a problem with the selection of the columns (this function selects dd$genes=ddaux$genes[,c(4,5,6)], that should correspond to "ControlType" "ProbeName" and "GeneName"), and you are using ddaux=read.maimages don?t use the readMicroRnaAFE at all. Try ddaux=read.maimages(...) and then use this ddaux object in your next calls to the functions, making first the selection of columns you want to do. What you are doing I guess is right, but change "dd$genes=ddaux$genes[,c(6,7,8)]" by "ddaux$genes=ddaux$genes[,c(6,7,8)]" ddaux=read.maimages(...) ddaux$genes=ddaux$genes[,c(6,7,8)]. HTH p.- -----Mensaje original----- De: Neel Aluru [mailto:naluru at whoi.edu] Enviado el: Monday, June 14, 2010 10:37 PM Para: Pedro L?pez Romero CC: bioc Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question Hi Pedro, I tried following your suggestions and I still an error message as, "FILTERING BY ControlType Error in data.frame(as.character(PROBE_ID), as.character(GENE_ID), as.character(probe.chr), : arguments imply differing number of rows: 231, 0". I looked into the mailing list archive and I saw one post there with similar issue but they were trying to modify the source code. I am not an expert in R and do not want to play with source code. If you have any suggestions on where the problem lies with the above error message, that will be great. Sorry to bother you on this. Thank you very much for your help. Sincerely, Neel The following is the session info. > library("AgiMicroRna") > targets.micro=readTargets(infile="targets.txt", verbose=TRUE) Target File FileName Treatment GErep Subject 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > ddaux=read.maimages(files=targets.micro$FileName,source="agilent", + + other.columns=list(IsGeneDetected="gIsGeneDetected", + + IsSaturated="gIsSaturated", + + IsFeatNonUnifOF="gIsFeatNonUnifOL", + + IsFeatPopnOL="gIsFeatPopnOL", + + ChrCoord="chr_coord", + + BGKmd="gBGMedianSignal", + + BGKus="gBGUsed"), + + columns=list(Rf="gTotalGeneSignal", + + Gf="gTotalProbeSignal", + + Rb="gMeanSignal", + + Gb="gProcessedSignal"), + + verbose=TRUE,sep="\t",quote="") Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt > names(ddaux) [1] "R" "G" "Rb" "Gb" "targets" "genes" "source" "other" > names(ddaux$genes) [1] "Row" "Col" "Start" "Sequence" "ProbeUID" "ControlType" [7] "ProbeName" "GeneName" "SystematicName" "Description" > dd=readMicroRnaAFE(targets.micro, verbose=TRUE) Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt RGList: dd$R: 'gTotalGeneSignal' dd$G: 'gTotalProbeSignal' dd$Rb: 'gMeanSignal' dd$Gb: 'gProcessedSignal' > dd$genes=ddaux$genes[,c(6,7,8)] > cvArray(dd, "MeanSignal", targets.micro, verbose=TRUE) Foreground: MeanSignal FILTERING BY ControlType FLAG RAW DATA: 5335 PROBES without CONTROLS: 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490 (Non-CTRL) Unique Genes: 231 ---------------------------------- DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20 18 36 416 ------------------------------------------------------ Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 60_TCDD_1 60_TCDD_2 0.078 0.081 0.091 0.081 0.077 0.067 0.076 0.066 0.103 0.073 0.086 60_TCDD_3 0.069 ------------------------------------------------------ DISTRIBUTION OF REPLICATED Noncontrol Genes reps 20 231 ------------------------------------------------------ > ddTGS.rma = rmaMicroRna(dd, normalize=TRUE, background=FALSE) Calculating Expression > ddPROC = filterMicroRna(ddTGS.rma, dd, control = TRUE, IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS FILTERING BY ControlType Error in data.frame(as.character(PROBE_ID), as.character(GENE_ID), as.character(probe.chr), : arguments imply differing number of rows: 231, 0 On Jun 2, 2010, at 5:32 AM, Pedro L?pez Romero wrote: > Hi Neel, > Try to use readMicroRnaAFE(targets,verbose=TRUE) to load your data into R instead of calling read.maimages() by yourself. This will solve your problem > > Cheers > > p.- > > > -----Mensaje original----- > De: Neel Aluru [mailto:naluru at whoi.edu] > Enviado el: Tuesday, June 01, 2010 7:34 PM > Para: Martin Morgan > CC: bioc; Pedro L?pez Romero > Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question > > Thanks, Martin. I have contacted Pedro today and hopefully he will get a chance to see my mail. In the mean time I will follow your suggestions. > > Thanks once again. > > Neel > > On Jun 1, 2010, at 1:31 PM, Martin Morgan wrote: > >> On 06/01/2010 06:43 AM, Neel Aluru wrote: >>> Hello, >>> >>> I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. > >>> >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >>> FILTERING PROBES BY FLAGS >>> >>> >>> FILTERING BY ControlType >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >>> ncol = dim(ddFILT)[2]) : >>> attempt to set an attribute on NULL >>> >>> >>> I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. >> >> Hi Neel -- I can't help with specifics, but >> >>> matrix(NULL) >> Error in matrix(NULL) : attempt to set an attribute on NULL >> >> so the proximate cause of the error message is likely that >> ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't >> exist. You can investigate this by inspecting the code, e.g., >> >>> options(error=browser()) >> >> and then re-running your code. See ?browser; when done use >> options(error=NULL). Before that I'd revisit the help page for this >> function and double-check that you are providing appropriate arguments. >> >> I've added >> >>> packageDescription('AgiMicroRna')$Maintainer >> [1] "Pedro Lopez-Romero <plopez at="" cnic.es="">" >> >> to the email, as Pedro in the best position to help you. >> >> Martin >> >>> Thank you very much, >>> >>> Neel >>> >>> >>> >>> >>> Session Info >>> >>>> library("AgiMicroRna") >>>> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) >>> >>> Target File >>> FileName Treatment GErep Subject >>> 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 >>> 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 >>> 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 >>> 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 >>> 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 >>> 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 >>> 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 >>> 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 >>> 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 >>> 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 >>> 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 >>> 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 >>> >>>> dd.micro=read.maimages(targets.micro$FileName, >>> columns=list(R="gTotalGeneSignal",G= >>> "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), >>> annotation=c("ProbeUID","ControlType","ProbeName","GeneName","Syst ematicName", >>> "sequence", "accessions","probe_mappings", >>> "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", >>> "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) >>> Read 36_DMSO_1.txt >>> Read 36_DMSO_2.txt >>> Read 36_DMSO_3.txt >>> Read 36_TCDD_1.txt >>> Read 36_TCDD_2.txt >>> Read 36_TCDD_3.txt >>> Read 60_DMSO_1.txt >>> Read 60_DMSO_2.txt >>> Read 60_DMSO_3.txt >>> Read 60_TCDD_1.txt >>> Read 60_TCDD_2.txt >>> Read 60_TCDD_3.txt >>>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) >>> Foreground: MeanSignal >>> >>> FILTERING BY ControlType FLAG >>> >>> RAW DATA: 5335 >>> PROBES without CONTROLS: 4620 >>> ---------------------------------- >>> (Non-CTRL) Unique Probe: 490 >>> (Non-CTRL) Unique Genes: 231 >>> ---------------------------------- >>> DISTRIBUTION OF REPLICATED NonControl Probes >>> reps >>> 5 6 7 10 >>> 20 18 36 416 >>> ------------------------------------------------------ >>> Replication at Probe level- MEDIAN CV >>> 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 >>> 60_DMSO_2 60_DMSO_3 >>> 0.078 0.081 0.091 0.081 0.077 0.067 >>> 0.076 0.066 0.103 >>> 60_TCDD_1 60_TCDD_2 60_TCDD_3 >>> 0.073 0.086 0.069 >>> ------------------------------------------------------ >>> DISTRIBUTION OF REPLICATED Noncontrol Genes >>> reps >>> 20 >>> 231 >>> ------------------------------------------------------ >>>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >>> Calculating Expression >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >>> FILTERING PROBES BY FLAGS >>> >>> >>> FILTERING BY ControlType >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >>> ncol = dim(ddFILT)[2]) : >>> attempt to set an attribute on NULL >>> >>>> MMM = ddTGS.rma$Rb >>>> colnames(MMM) = colnames(dd.micro$Rb) >>>> maintitle='TGS.rma' >>>> colorfill='blue' >>>> ddaux=ddTGS.rma >>>> ddaux$G=MMM >>>> mvaMicroRna(ddaux, maintitle, verbose=TRUE) >>> >>> ------------------------------------------------------ >>> mvaMicroRna info: >>> FEATURES : 231 >>> POSITIVE CTRL: 12 >>> NEGATIVE CTRL: 7 >>> STRUCTURAL: 3 >>>> rm(ddaux) >>>> RleMicroRna(MMM,"RLE TGS.rma", colorfill) >>>> boxplotMicroRna(MMM, maintitle, colorfill) >>>> plotDensityMicroRna(MMM, maintitle) >>>> spottypes = readSpotTypes() >>>> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) >>> Matching patterns for: ProbeName GeneName >>> Found 231 gene >>> Found 1 BLANK >>> Found 1 Blank >>> Found 0 blank >>> Found 6 positive >>> Found 0 negative >>> Found 0 flag1 >>> Found 0 flag2 >>> Found 6 flag3 >>> Found 5 flag4 >>> Found 1 flag5 >>> Setting attributes: values >>>> i = ddTGS.rma$genes$Status == "gene" >>>> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, >>> makePLOT=TRUE, verbose = TRUE) >>> outPUT DATA: esetPROC >>> Features Samples >>> 231 12 >>>> design=model.matrix(~-1+treatment) >>>> print(design) >>> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD >>> 1 1 0 0 0 >>> 2 1 0 0 0 >>> 3 1 0 0 0 >>> 4 0 1 0 0 >>> 5 0 1 0 0 >>> 6 0 1 0 0 >>> 7 0 0 1 0 >>> 8 0 0 1 0 >>> 9 0 0 1 0 >>> 10 0 0 0 1 >>> 11 0 0 0 1 >>> 12 0 0 0 1 >>> attr(,"assign") >>> [1] 1 1 1 1 >>> attr(,"contrasts") >>> attr(,"contrasts")$treatment >>> [1] "contr.treatment" >>> >>>> fit=lmFit(esetPROC, design) >>>> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = >>> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = >>> treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = >>> treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = >>> treatment60DMSO-treatment36DMSO, levels=design) >>>> print(cont.matrix) >>> Contrasts >>> Levels treatment36TCDDvstreatment36DMSO >>> treatment60TCDDvstreatment60DMSO >>> treatment36DMSO -1 >>> 0 >>> treatment36TCDD 1 >>> 0 >>> treatment60DMSO 0 >>> -1 >>> treatment60TCDD 0 >>> 1 >>> Contrasts >>> Levels treatment60TCDDvstreatment36TCDD >>> treatment60DMSOvstreatment36DMSO >>> treatment36DMSO 0 >>> -1 >>> treatment36TCDD -1 >>> 0 >>> treatment60DMSO 0 >>> 1 >>> treatment60TCDD 1 >>> 0 >>>> fit2 = contrasts.fit(fit,cont.matrix) >>>> print(head(fit2$coeff)) >>> Contrasts >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >>> dre-let-7a 0.038640984 0.013333873 >>> dre-let-7b 0.074038749 -0.031608286 >>> dre-let-7c 0.026244357 -0.005682488 >>> dre-let-7d 0.067340768 0.055567054 >>> dre-let-7e 0.004569306 0.136348664 >>> dre-let-7f 0.042880109 0.085568058 >>> Contrasts >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >>> dre-let-7a 1.7358343 1.76114142 >>> dre-let-7b 0.1366920 0.24233899 >>> dre-let-7c 0.9920976 1.02402449 >>> dre-let-7d 0.8098432 0.82161694 >>> dre-let-7e 0.1186829 -0.01309647 >>> dre-let-7f 1.1245878 1.08189990 >>>> fit2 = eBayes(fit2) >>>> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) >>> DATA >>> Features Samples >>> 231 12 >>> >>>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = >>> "BH", PVcut = 0.1, verbose = TRUE) >>> >>> ------------------------------------------------------ >>> Method for Selecting DEGs: separate >>> Multiple Testing method: BH - pval 0.1 >>> >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >>> UP 0 5 >>> DOWN 0 1 >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >>> UP 56 51 >>> DOWN 80 91 >>> ------------------------------------------------------ >>>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", >>> MTestmethod="BH",cont.matrix, verbose= TRUE) >>>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, >>> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = >>> 0.1, Mcut=0, verbose=TRUE) >>> ------------------------------------------------------ >>> CONTRAST: 1 - treatment36TCDDvstreatment36DMSO >>> >>> Error in data.frame(PROBE_ID, as.character(GENE_ID), >>> as.character(chr_coord), : >>> arguments imply differing number of rows: 231, 0 >>> >>> >>> >>> >>> Neel Aluru >>> Postdoctoral Scholar >>> Biology Department >>> Woods Hole Oceanographic Institution >>> Woods Hole, MA 02543 >>> USA >>> 508-289-3607 >>> >>> _______________________________________________ >>> 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 >> >> >> -- >> Martin Morgan >> Computational Biology / Fred Hutchinson Cancer Research Center >> 1100 Fairview Ave. N. >> PO Box 19024 Seattle, WA 98109 >> >> Location: Arnold Building M1 B861 >> Phone: (206) 667-2793 >> > > Neel Aluru > Postdoctoral Scholar > Biology Department > Woods Hole Oceanographic Institution > Woods Hole, MA 02543 > USA > 508-289-3607 > > > > > *************** AVISO LEGAL *************** > Este mensaje va dirigido, de manera exclusiva, a su destinatario y > contiene informaci?n confidencial y sujeta al secreto profesional, > cuya divulgaci?n no est? permitida por la ley. 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Si no > consintiese la utilizaci?n del correo electr?nico o de las > comunicaciones v?a Internet le rogamos nos lo comunique y ponga en > nuestro conocimiento de manera inmediata. > > *************** LEGAL NOTICE ************** > This message is intended exclusively for the person to whom it is > addressed and contains privileged and confidential information > protected from disclosure by law. If you are not the addressee > indicated in this message, you should immediately delete it and any > attachments and notify the sender by reply e-mail or by phone > (+34 914531200). In such case, you are hereby notified that any > dissemination, distribution, copying or use of this message or any > attachments, for any purpose, is strictly prohibited by law. 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Si no consintiese la utilizaci?n del correo electr?nico o de las comunicaciones v?a Internet le rogamos nos lo comunique y ponga en nuestro conocimiento de manera inmediata. *************** LEGAL NOTICE ************** This message is intended exclusively for the person to whom it is addressed and contains privileged and confidential information protected from disclosure by law. If you are not the addressee indicated in this message, you should immediately delete it and any attachments and notify the sender by reply e-mail or by phone (+34 914531200). In such case, you are hereby notified that any dissemination, distribution, copying or use of this message or any attachments, for any purpose, is strictly prohibited by law. We hereby inform you, as addressee of this message, that e-mail and Internet do not guarantee the confidentiality, nor the completeness or proper reception of the messages sent and, thus, CNIC does not assume any liability for those circumstances. Should you not agree to the use of e-mail or to communications via Internet, you are kindly requested to notify us immediately.
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@james-w-macdonald-5106
Last seen 17 hours ago
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Hi Neel, Neel Aluru wrote: > Hello, > > I have asked this question before and haven't heard from anyone. > Sorry for reposting it as I spent lot of time on it and still cannot > figure it out. I need to filter the data before statistical analysis > so as to remove the genes that are not detected. Have you emailed the maintainer of this package directly? He may not subscribe to this list, or he may have simply missed your first email. > >> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, > IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, > limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) > FILTERING PROBES BY FLAGS > > > FILTERING BY ControlType Error in > matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], ncol = > dim(ddFILT)[2]) : attempt to set an attribute on NULL > > > I checked my data files to see if the required column > (IsGeneDetected) is present and it is there. But, for some reason it > is not detecting and I do not understand the error message I am > getting. If anyone can explain the error message to me that would be > great. I have posted the session info below. The required column is called gIsGeneDetected. Is that there? Also, when people want your sessionInfo, they usually mean for you to run sessionInfo() after you have loaded all the packages you are using. Although showing what you have done as below could be helpful as well. Best, Jim > > Thank you very much, > > Neel > > > > > Session Info > >> library("AgiMicroRna") >> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) > > Target File FileName Treatment GErep Subject 36_DMSO_1 36_DMSO_1.txt > 36DMSO 1 1 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 > 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 36_TCDD_1 > 36_TCDD_1.txt 36TCDD 2 1 36_TCDD_2 36_TCDD_2.txt > 36TCDD 2 2 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 > 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 60_DMSO_2 > 60_DMSO_2.txt 60DMSO 3 2 60_DMSO_3 60_DMSO_3.txt > 60DMSO 3 3 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 > 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 60_TCDD_3 > 60_TCDD_3.txt 60TCDD 4 3 > >> dd.micro=read.maimages(targets.micro$FileName, > columns=list(R="gTotalGeneSignal",G= > "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), > annotation=c("ProbeUID","ControlType","ProbeName","GeneName","System aticName", > "sequence", "accessions","probe_mappings", > "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", > "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) Read > 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read > 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read > 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read > 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt >> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) > Foreground: MeanSignal > > FILTERING BY ControlType FLAG > > RAW DATA: 5335 PROBES without CONTROLS: > 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490 > (Non-CTRL) Unique Genes: 231 ---------------------------------- > DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20 > 18 36 416 ------------------------------------------------------ > Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3 > 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 0.078 > 0.081 0.091 0.081 0.077 0.067 0.076 0.066 > 0.103 60_TCDD_1 60_TCDD_2 60_TCDD_3 0.073 0.086 0.069 > ------------------------------------------------------ DISTRIBUTION > OF REPLICATED Noncontrol Genes reps 20 231 > ------------------------------------------------------ >> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >> > Calculating Expression >> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, > IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, > limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) > FILTERING PROBES BY FLAGS > > > FILTERING BY ControlType Error in > matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], ncol = > dim(ddFILT)[2]) : attempt to set an attribute on NULL > >> MMM = ddTGS.rma$Rb colnames(MMM) = colnames(dd.micro$Rb) >> maintitle='TGS.rma' colorfill='blue' ddaux=ddTGS.rma ddaux$G=MMM >> mvaMicroRna(ddaux, maintitle, verbose=TRUE) > > ------------------------------------------------------ mvaMicroRna > info: FEATURES : 231 POSITIVE CTRL: 12 NEGATIVE CTRL: > 7 STRUCTURAL: 3 >> rm(ddaux) RleMicroRna(MMM,"RLE TGS.rma", colorfill) >> boxplotMicroRna(MMM, maintitle, colorfill) plotDensityMicroRna(MMM, >> maintitle) spottypes = readSpotTypes() ddTGS.rma$genes$Status = >> controlStatus(spottypes, ddTGS.rma) > Matching patterns for: ProbeName GeneName Found 231 gene Found 1 > BLANK Found 1 Blank Found 0 blank Found 6 positive Found 0 negative > Found 0 flag1 Found 0 flag2 Found 6 flag3 Found 5 flag4 Found 1 flag5 > Setting attributes: values >> i = ddTGS.rma$genes$Status == "gene" esetPROC = >> esetMicroRna(ddTGS.rma[i,], targets.micro, > makePLOT=TRUE, verbose = TRUE) outPUT DATA: esetPROC Features > Samples 231 12 >> design=model.matrix(~-1+treatment) print(design) > treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD 1 > 1 0 0 0 2 1 > 0 0 0 3 1 0 > 0 0 4 0 1 0 > 0 5 0 1 0 0 > 6 0 1 0 0 7 > 0 0 1 0 8 0 > 0 1 0 9 0 0 > 1 0 10 0 0 0 > 1 11 0 0 0 1 > 12 0 0 0 1 > attr(,"assign") [1] 1 1 1 1 attr(,"contrasts") > attr(,"contrasts")$treatment [1] "contr.treatment" > >> fit=lmFit(esetPROC, design) cont.matrix = >> makeContrasts(treatment36TCDDvstreatment36DMSO = > treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = > treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = > treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = > treatment60DMSO-treatment36DMSO, levels=design) >> print(cont.matrix) > Contrasts Levels treatment36TCDDvstreatment36DMSO > treatment60TCDDvstreatment60DMSO treatment36DMSO > -1 0 treatment36TCDD 1 0 > treatment60DMSO 0 -1 treatment60TCDD > 0 1 Contrasts Levels treatment60TCDDvstreatment36TCDD > treatment60DMSOvstreatment36DMSO treatment36DMSO > 0 -1 treatment36TCDD -1 0 > treatment60DMSO 0 1 treatment60TCDD > 1 0 >> fit2 = contrasts.fit(fit,cont.matrix) print(head(fit2$coeff)) > Contrasts treatment36TCDDvstreatment36DMSO > treatment60TCDDvstreatment60DMSO dre-let-7a > 0.038640984 0.013333873 dre-let-7b > 0.074038749 -0.031608286 dre-let-7c > 0.026244357 -0.005682488 dre-let-7d > 0.067340768 0.055567054 dre-let-7e > 0.004569306 0.136348664 dre-let-7f > 0.042880109 0.085568058 Contrasts > treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO > dre-let-7a 1.7358343 > 1.76114142 dre-let-7b 0.1366920 > 0.24233899 dre-let-7c 0.9920976 > 1.02402449 dre-let-7d 0.8098432 > 0.82161694 dre-let-7e 0.1186829 > -0.01309647 dre-let-7f 1.1245878 > 1.08189990 >> fit2 = eBayes(fit2) fit2 = basicLimma(esetPROC, design, >> cont.matrix, verbose = TRUE) > DATA Features Samples 231 12 > >> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = > "BH", PVcut = 0.1, verbose = TRUE) > > ------------------------------------------------------ Method for > Selecting DEGs: separate Multiple Testing method: BH - pval 0.1 > > treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO UP > 0 5 DOWN > 0 1 treatment60TCDDvstreatment36TCDD > treatment60DMSOvstreatment36DMSO UP > 56 51 DOWN > 80 91 > ------------------------------------------------------ >> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", > MTestmethod="BH",cont.matrix, verbose= TRUE) >> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, > cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = > 0.1, Mcut=0, verbose=TRUE) > ------------------------------------------------------ CONTRAST: 1 > - treatment36TCDDvstreatment36DMSO > > Error in data.frame(PROBE_ID, as.character(GENE_ID), > as.character(chr_coord), : arguments imply differing number of rows: > 231, 0 > > > > > Neel Aluru Postdoctoral Scholar Biology Department Woods Hole > Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607 > > _______________________________________________ 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 -- 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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Thanks, James. I will email Pedro directly. I have the column gIsGeneDetected in my files. I checked it. I have loaded all the packages necessary. Most of the functions in AgiMicroRna are from Affy and Limma packages. Thank you for your suggestion. Sincerely, Neel On Jun 1, 2010, at 10:39 AM, James W. MacDonald wrote: > Hi Neel, > > Neel Aluru wrote: >> Hello, >> I have asked this question before and haven't heard from anyone. >> Sorry for reposting it as I spent lot of time on it and still cannot >> figure it out. I need to filter the data before statistical analysis >> so as to remove the genes that are not detected. > > Have you emailed the maintainer of this package directly? He may not subscribe to this list, or he may have simply missed your first email. > >>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS >> FILTERING BY ControlType Error in >> matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], ncol = >> dim(ddFILT)[2]) : attempt to set an attribute on NULL >> I checked my data files to see if the required column >> (IsGeneDetected) is present and it is there. But, for some reason it >> is not detecting and I do not understand the error message I am >> getting. If anyone can explain the error message to me that would be >> great. I have posted the session info below. > > The required column is called gIsGeneDetected. Is that there? > > Also, when people want your sessionInfo, they usually mean for you to run sessionInfo() after you have loaded all the packages you are using. Although showing what you have done as below could be helpful as well. > > Best, > > Jim > > > >> Thank you very much, >> Neel >> Session Info >>> library("AgiMicroRna") targets.micro=readTargets(infile="targets.txt", verbose=TRUE) >> Target File FileName Treatment GErep Subject 36_DMSO_1 36_DMSO_1.txt >> 36DMSO 1 1 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 36_TCDD_1 >> 36_TCDD_1.txt 36TCDD 2 1 36_TCDD_2 36_TCDD_2.txt >> 36TCDD 2 2 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 60_DMSO_2 >> 60_DMSO_2.txt 60DMSO 3 2 60_DMSO_3 60_DMSO_3.txt >> 60DMSO 3 3 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 60_TCDD_3 >> 60_TCDD_3.txt 60TCDD 4 3 >>> dd.micro=read.maimages(targets.micro$FileName, >> columns=list(R="gTotalGeneSignal",G= "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), annotati on=c("ProbeUID","ControlType","ProbeName","GeneName","SystematicName", >> "sequence", "accessions","probe_mappings", "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) Read >> 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read >> 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read >> 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read >> 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt >>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) >> Foreground: MeanSignal >> FILTERING BY ControlType FLAG >> RAW DATA: 5335 PROBES without CONTROLS: >> 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490 >> (Non-CTRL) Unique Genes: 231 ---------------------------------- DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20 >> 18 36 416 ------------------------------------------------------ Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3 >> 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 0.078 >> 0.081 0.091 0.081 0.077 0.067 0.076 0.066 >> 0.103 60_TCDD_1 60_TCDD_2 60_TCDD_3 0.073 0.086 0.069 ------------------------------------------------------ DISTRIBUTION >> OF REPLICATED Noncontrol Genes reps 20 231 ------------------------------------------------------ >>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >> Calculating Expression >>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS >> FILTERING BY ControlType Error in >> matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], ncol = >> dim(ddFILT)[2]) : attempt to set an attribute on NULL >>> MMM = ddTGS.rma$Rb colnames(MMM) = colnames(dd.micro$Rb) maintitle='TGS.rma' colorfill='blue' ddaux=ddTGS.rma ddaux$G=MMM mvaMicroRna(ddaux, maintitle, verbose=TRUE) >> ------------------------------------------------------ mvaMicroRna >> info: FEATURES : 231 POSITIVE CTRL: 12 NEGATIVE CTRL: >> 7 STRUCTURAL: 3 >>> rm(ddaux) RleMicroRna(MMM,"RLE TGS.rma", colorfill) boxplotMicroRna(MMM, maintitle, colorfill) plotDensityMicroRna(MMM, >>> maintitle) spottypes = readSpotTypes() ddTGS.rma$genes$Status = >>> controlStatus(spottypes, ddTGS.rma) >> Matching patterns for: ProbeName GeneName Found 231 gene Found 1 >> BLANK Found 1 Blank Found 0 blank Found 6 positive Found 0 negative Found 0 flag1 Found 0 flag2 Found 6 flag3 Found 5 flag4 Found 1 flag5 >> Setting attributes: values >>> i = ddTGS.rma$genes$Status == "gene" esetPROC = >>> esetMicroRna(ddTGS.rma[i,], targets.micro, >> makePLOT=TRUE, verbose = TRUE) outPUT DATA: esetPROC Features >> Samples 231 12 >>> design=model.matrix(~-1+treatment) print(design) >> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD 1 >> 1 0 0 0 2 1 >> 0 0 0 3 1 0 >> 0 0 4 0 1 0 >> 0 5 0 1 0 0 6 0 1 0 0 7 >> 0 0 1 0 8 0 >> 0 1 0 9 0 0 >> 1 0 10 0 0 0 >> 1 11 0 0 0 1 12 0 0 0 1 attr(,"assign") [1] 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$treatment [1] "contr.treatment" >>> fit=lmFit(esetPROC, design) cont.matrix = >>> makeContrasts(treatment36TCDDvstreatment36DMSO = >> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = treatment60DMSO-treatment36DMSO, levels=design) >>> print(cont.matrix) >> Contrasts Levels treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO treatment36DMSO >> -1 0 treatment36TCDD 1 0 treatment60DMSO 0 -1 treatment60TCDD >> 0 1 Contrasts Levels treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO treatment36DMSO >> 0 -1 treatment36TCDD -1 0 treatment60DMSO 0 1 treatment60TCDD >> 1 0 >>> fit2 = contrasts.fit(fit,cont.matrix) print(head(fit2$coeff)) >> Contrasts treatment36TCDDvstreatment36DMSO >> treatment60TCDDvstreatment60DMSO dre-let-7a >> 0.038640984 0.013333873 dre-let-7b >> 0.074038749 -0.031608286 dre-let-7c >> 0.026244357 -0.005682488 dre-let-7d >> 0.067340768 0.055567054 dre-let-7e >> 0.004569306 0.136348664 dre-let-7f >> 0.042880109 0.085568058 Contrasts treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO dre- let-7a 1.7358343 >> 1.76114142 dre-let-7b 0.1366920 >> 0.24233899 dre-let-7c 0.9920976 >> 1.02402449 dre-let-7d 0.8098432 >> 0.82161694 dre-let-7e 0.1186829 >> -0.01309647 dre-let-7f 1.1245878 >> 1.08189990 >>> fit2 = eBayes(fit2) fit2 = basicLimma(esetPROC, design, >>> cont.matrix, verbose = TRUE) >> DATA Features Samples 231 12 >>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = >> "BH", PVcut = 0.1, verbose = TRUE) >> ------------------------------------------------------ Method for >> Selecting DEGs: separate Multiple Testing method: BH - pval 0.1 >> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO UP >> 0 5 DOWN >> 0 1 treatment60TCDDvstreatment36TCDD >> treatment60DMSOvstreatment36DMSO UP >> 56 51 DOWN >> 80 91 ------------------------------------------------------ >>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", >> MTestmethod="BH",cont.matrix, verbose= TRUE) >>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, >> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = 0.1, Mcut=0, verbose=TRUE) ------------------------------------------------------ CONTRAST: 1 >> - treatment36TCDDvstreatment36DMSO >> Error in data.frame(PROBE_ID, as.character(GENE_ID), as.character(chr_coord), : arguments imply differing number of rows: >> 231, 0 >> Neel Aluru Postdoctoral Scholar Biology Department Woods Hole >> Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607 >> _______________________________________________ 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 > > -- > 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 Neel Aluru Postdoctoral Scholar Biology Department Woods Hole Oceanographic Institution Woods Hole, MA 02543 USA 508-289-3607
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Neel Aluru ▴ 460
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Hi Pedro, Thank you for the reply. I did try using readMicroRnaAFE function and it gives more error messages. I used Agilent microRNA arrays and scanned them using Axon 4000B scanner. Data was extracted using 9.5. version of Agilent feature extraction software. I am mentioning these just to let you know if any of these make any difference to the data. I have all the columns but for some reason they are not recognized by the program. Here is the session info using readMicroRnaAFE function. It will not calculate CV and also the normalization. That's why I used read.ma images function. Other than this minor glitch, this package was very easy to understand and use. Thank you very much for writing this package. Sincerely, Neel Session info () > setwd("/Users/Neel/Desktop/miRNAarray") > targets.micro=readTargets(infile="targets.txt", verbose=TRUE) Target File FileName Treatment GErep Subject 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > dd.micro = readMicroRnaAFE(targets.micro,verbose=TRUE) Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt RGList: dd$R: 'gTotalGeneSignal' dd$G: 'gTotalProbeSignal' dd$Rb: 'gMeanSignal' dd$Gb: 'gProcessedSignal' > cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) Foreground: MeanSignal FILTERING BY ControlType FLAG RAW DATA: 5335 PROBES without CONTROLS: 4620 Error in cvArray(dd.micro, "MeanSignal", targets.micro, verbose = TRUE) : NOT DUPLICATED ProbeName in chip > ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) Error in if (min(dd.aux$Rb) < 0) { : missing value where TRUE/FALSE needed On Jun 2, 2010, at 5:32 AM, Pedro L?pez Romero wrote: > Hi Neel, > Try to use readMicroRnaAFE(targets,verbose=TRUE) to load your data into R instead of calling read.maimages() by yourself. This will solve your problem > > Cheers > > p.- > > > -----Mensaje original----- > De: Neel Aluru [mailto:naluru at whoi.edu] > Enviado el: Tuesday, June 01, 2010 7:34 PM > Para: Martin Morgan > CC: bioc; Pedro L?pez Romero > Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question > > Thanks, Martin. I have contacted Pedro today and hopefully he will get a chance to see my mail. In the mean time I will follow your suggestions. > > Thanks once again. > > Neel > > On Jun 1, 2010, at 1:31 PM, Martin Morgan wrote: > >> On 06/01/2010 06:43 AM, Neel Aluru wrote: >>> Hello, >>> >>> I have asked this question before and haven't heard from anyone. Sorry for reposting it as I spent lot of time on it and still cannot figure it out. I need to filter the data before statistical analysis so as to remove the genes that are not detected. > >>> >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >>> FILTERING PROBES BY FLAGS >>> >>> >>> FILTERING BY ControlType >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >>> ncol = dim(ddFILT)[2]) : >>> attempt to set an attribute on NULL >>> >>> >>> I checked my data files to see if the required column (IsGeneDetected) is present and it is there. But, for some reason it is not detecting and I do not understand the error message I am getting. If anyone can explain the error message to me that would be great. I have posted the session info below. >> >> Hi Neel -- I can't help with specifics, but >> >>> matrix(NULL) >> Error in matrix(NULL) : attempt to set an attribute on NULL >> >> so the proximate cause of the error message is likely that >> ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't >> exist. You can investigate this by inspecting the code, e.g., >> >>> options(error=browser()) >> >> and then re-running your code. See ?browser; when done use >> options(error=NULL). Before that I'd revisit the help page for this >> function and double-check that you are providing appropriate arguments. >> >> I've added >> >>> packageDescription('AgiMicroRna')$Maintainer >> [1] "Pedro Lopez-Romero <plopez at="" cnic.es="">" >> >> to the email, as Pedro in the best position to help you. >> >> Martin >> >>> Thank you very much, >>> >>> Neel >>> >>> >>> >>> >>> Session Info >>> >>>> library("AgiMicroRna") >>>> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) >>> >>> Target File >>> FileName Treatment GErep Subject >>> 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 >>> 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 >>> 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 >>> 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 >>> 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 >>> 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 >>> 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 >>> 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 >>> 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 >>> 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 >>> 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 >>> 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 >>> >>>> dd.micro=read.maimages(targets.micro$FileName, >>> columns=list(R="gTotalGeneSignal",G= >>> "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), >>> annotation=c("ProbeUID","ControlType","ProbeName","GeneName","Syst ematicName", >>> "sequence", "accessions","probe_mappings", >>> "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", >>> "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) >>> Read 36_DMSO_1.txt >>> Read 36_DMSO_2.txt >>> Read 36_DMSO_3.txt >>> Read 36_TCDD_1.txt >>> Read 36_TCDD_2.txt >>> Read 36_TCDD_3.txt >>> Read 60_DMSO_1.txt >>> Read 60_DMSO_2.txt >>> Read 60_DMSO_3.txt >>> Read 60_TCDD_1.txt >>> Read 60_TCDD_2.txt >>> Read 60_TCDD_3.txt >>>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) >>> Foreground: MeanSignal >>> >>> FILTERING BY ControlType FLAG >>> >>> RAW DATA: 5335 >>> PROBES without CONTROLS: 4620 >>> ---------------------------------- >>> (Non-CTRL) Unique Probe: 490 >>> (Non-CTRL) Unique Genes: 231 >>> ---------------------------------- >>> DISTRIBUTION OF REPLICATED NonControl Probes >>> reps >>> 5 6 7 10 >>> 20 18 36 416 >>> ------------------------------------------------------ >>> Replication at Probe level- MEDIAN CV >>> 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 >>> 60_DMSO_2 60_DMSO_3 >>> 0.078 0.081 0.091 0.081 0.077 0.067 >>> 0.076 0.066 0.103 >>> 60_TCDD_1 60_TCDD_2 60_TCDD_3 >>> 0.073 0.086 0.069 >>> ------------------------------------------------------ >>> DISTRIBUTION OF REPLICATED Noncontrol Genes >>> reps >>> 20 >>> 231 >>> ------------------------------------------------------ >>>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) >>> Calculating Expression >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >>> FILTERING PROBES BY FLAGS >>> >>> >>> FILTERING BY ControlType >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], >>> ncol = dim(ddFILT)[2]) : >>> attempt to set an attribute on NULL >>> >>>> MMM = ddTGS.rma$Rb >>>> colnames(MMM) = colnames(dd.micro$Rb) >>>> maintitle='TGS.rma' >>>> colorfill='blue' >>>> ddaux=ddTGS.rma >>>> ddaux$G=MMM >>>> mvaMicroRna(ddaux, maintitle, verbose=TRUE) >>> >>> ------------------------------------------------------ >>> mvaMicroRna info: >>> FEATURES : 231 >>> POSITIVE CTRL: 12 >>> NEGATIVE CTRL: 7 >>> STRUCTURAL: 3 >>>> rm(ddaux) >>>> RleMicroRna(MMM,"RLE TGS.rma", colorfill) >>>> boxplotMicroRna(MMM, maintitle, colorfill) >>>> plotDensityMicroRna(MMM, maintitle) >>>> spottypes = readSpotTypes() >>>> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) >>> Matching patterns for: ProbeName GeneName >>> Found 231 gene >>> Found 1 BLANK >>> Found 1 Blank >>> Found 0 blank >>> Found 6 positive >>> Found 0 negative >>> Found 0 flag1 >>> Found 0 flag2 >>> Found 6 flag3 >>> Found 5 flag4 >>> Found 1 flag5 >>> Setting attributes: values >>>> i = ddTGS.rma$genes$Status == "gene" >>>> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, >>> makePLOT=TRUE, verbose = TRUE) >>> outPUT DATA: esetPROC >>> Features Samples >>> 231 12 >>>> design=model.matrix(~-1+treatment) >>>> print(design) >>> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD >>> 1 1 0 0 0 >>> 2 1 0 0 0 >>> 3 1 0 0 0 >>> 4 0 1 0 0 >>> 5 0 1 0 0 >>> 6 0 1 0 0 >>> 7 0 0 1 0 >>> 8 0 0 1 0 >>> 9 0 0 1 0 >>> 10 0 0 0 1 >>> 11 0 0 0 1 >>> 12 0 0 0 1 >>> attr(,"assign") >>> [1] 1 1 1 1 >>> attr(,"contrasts") >>> attr(,"contrasts")$treatment >>> [1] "contr.treatment" >>> >>>> fit=lmFit(esetPROC, design) >>>> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = >>> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = >>> treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = >>> treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = >>> treatment60DMSO-treatment36DMSO, levels=design) >>>> print(cont.matrix) >>> Contrasts >>> Levels treatment36TCDDvstreatment36DMSO >>> treatment60TCDDvstreatment60DMSO >>> treatment36DMSO -1 >>> 0 >>> treatment36TCDD 1 >>> 0 >>> treatment60DMSO 0 >>> -1 >>> treatment60TCDD 0 >>> 1 >>> Contrasts >>> Levels treatment60TCDDvstreatment36TCDD >>> treatment60DMSOvstreatment36DMSO >>> treatment36DMSO 0 >>> -1 >>> treatment36TCDD -1 >>> 0 >>> treatment60DMSO 0 >>> 1 >>> treatment60TCDD 1 >>> 0 >>>> fit2 = contrasts.fit(fit,cont.matrix) >>>> print(head(fit2$coeff)) >>> Contrasts >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >>> dre-let-7a 0.038640984 0.013333873 >>> dre-let-7b 0.074038749 -0.031608286 >>> dre-let-7c 0.026244357 -0.005682488 >>> dre-let-7d 0.067340768 0.055567054 >>> dre-let-7e 0.004569306 0.136348664 >>> dre-let-7f 0.042880109 0.085568058 >>> Contrasts >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >>> dre-let-7a 1.7358343 1.76114142 >>> dre-let-7b 0.1366920 0.24233899 >>> dre-let-7c 0.9920976 1.02402449 >>> dre-let-7d 0.8098432 0.82161694 >>> dre-let-7e 0.1186829 -0.01309647 >>> dre-let-7f 1.1245878 1.08189990 >>>> fit2 = eBayes(fit2) >>>> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) >>> DATA >>> Features Samples >>> 231 12 >>> >>>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = >>> "BH", PVcut = 0.1, verbose = TRUE) >>> >>> ------------------------------------------------------ >>> Method for Selecting DEGs: separate >>> Multiple Testing method: BH - pval 0.1 >>> >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO >>> UP 0 5 >>> DOWN 0 1 >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO >>> UP 56 51 >>> DOWN 80 91 >>> ------------------------------------------------------ >>>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", >>> MTestmethod="BH",cont.matrix, verbose= TRUE) >>>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, >>> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = >>> 0.1, Mcut=0, verbose=TRUE) >>> ------------------------------------------------------ >>> CONTRAST: 1 - treatment36TCDDvstreatment36DMSO >>> >>> Error in data.frame(PROBE_ID, as.character(GENE_ID), >>> as.character(chr_coord), : >>> arguments imply differing number of rows: 231, 0 >>> >>> >>> >>> >>> Neel Aluru >>> Postdoctoral Scholar >>> Biology Department >>> Woods Hole Oceanographic Institution >>> Woods Hole, MA 02543 >>> USA >>> 508-289-3607 >>> >>> _______________________________________________ >>> 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 >> >> >> -- >> Martin Morgan >> Computational Biology / Fred Hutchinson Cancer Research Center >> 1100 Fairview Ave. N. >> PO Box 19024 Seattle, WA 98109 >> >> Location: Arnold 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Neel Aluru ▴ 460
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Hi Pedro, Thanks for the reply. I understand what you mean. I was able to solve the issue of reading the data and cvArray function by going through your replies in the mailing list. The problem I am having now is with the FilterMicroRna function. It doesn't matter how I read the data (either with read.maimages or AgiMicroRnaAFE), the filtering function is giving error messages. Here is the session info: > library("AgiMicroRna") > targets.micro=readTargets(infile="targets.txt", verbose=TRUE) > ddaux=read.maimages(files=targets.micro$FileName,source="agilent", other.columns=list(IsGeneDetected="gIsGeneDetected", IsSaturated="gIsSaturated", IsFeatNonUnifOF="gIsFeatNonUnifOL", IsFeatPopnOL="gIsFeatPopnOL", ChrCoord="chr_coord", BGKmd="gBGMedianSignal", BGKus="gBGUsed"), columns=list(Rf="gTotalGeneSignal", Gf="gTotalProbeSignal", Rb="gMeanSignal",Gb="gProcessedSignal"), verbose=TRUE,sep="\t",quote="") Read 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt > names(ddaux) [1] "R" "G" "Rb" "Gb" "targets" "genes" "source" [8] "other" > names(ddaux$genes) [1] "Row" "Col" "Start" "Sequence" [5] "ProbeUID" "ControlType" "ProbeName" "GeneName" [9] "SystematicName" "Description" > ddaux$genes=ddaux$genes[,c(6,7,8)] > cvArray(ddaux, "MeanSignal", targets.micro, verbose=TRUE) Foreground: MeanSignal FILTERING BY ControlType FLAG RAW DATA: 5335 PROBES without CONTROLS: 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490 (Non-CTRL) Unique Genes: 231 ---------------------------------- DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20 18 36 416 ------------------------------------------------------ Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 0.078 0.081 0.091 0.081 0.077 0.067 0.076 0.066 60_DMSO_3 60_TCDD_1 60_TCDD_2 60_TCDD_3 0.103 0.073 0.086 0.069 ------------------------------------------------------ DISTRIBUTION OF REPLICATED Noncontrol Genes reps 20 231 ------------------------------------------------------ > ddTGS.rma = rmaMicroRna(ddaux, normalize=TRUE, background=FALSE) Calculating Expression > ddPROC = filterMicroRna(ddTGS.rma, ddaux, control = TRUE, IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS FILTERING BY ControlType Error in data.frame(as.character(PROBE_ID), as.character(GENE_ID), as.character(probe.chr), : arguments imply differing number of rows: 231, 0 At 02:21 AM 6/16/2010, Pedro López Romero wrote: >Hi, > >I don´t understand why you are using >ddaux=read.maimages and then >dd=readMicroRnaAFE(targets.micro, verbose=TRUE). >If readMicroRnaAFE is giving you a problem with >the selection of the columns (this function >selects dd$genes=ddaux$genes[,c(4,5,6)], that >should correspond to "ControlType" "ProbeName" >and "GeneName"), and you are using >ddaux=read.maimages don´t use the readMicroRnaAFE at all. > >Try ddaux=read.maimages(...) and then use this >ddaux object in your next calls to the >functions, making first the selection of columns >you want to do. What you are doing I guess is >right, but change >"dd$genes=ddaux$genes[,c(6,7,8)]" by "ddaux$genes=ddaux$genes[,c(6,7,8)]" > >ddaux=read.maimages(...) >ddaux$genes=ddaux$genes[,c(6,7,8)]. > >HTH > >p.- > > >-----Mensaje original----- >De: Neel Aluru [mailto:naluru@whoi.edu] >Enviado el: Monday, June 14, 2010 10:37 PM >Para: Pedro López Romero >CC: bioc >Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question > >Hi Pedro, > >I tried following your suggestions and I still >an error message as, "FILTERING BY ControlType >Error in data.frame(as.character(PROBE_ID), >as.character(GENE_ID), >as.character(probe.chr), : arguments imply differing number of rows: 231, 0". > >I looked into the mailing list archive and I saw >one post there with similar issue but they were >trying to modify the source code. I am not an >expert in R and do not want to play with source code. > >If you have any suggestions on where the problem >lies with the above error message, that will be >great. Sorry to bother you on this. > >Thank you very much for your help. > >Sincerely, Neel > > The following is the session info. > > > library("AgiMicroRna") > > targets.micro=readTargets(infile="targets.txt", verbose=TRUE) > >Target File > FileName Treatment GErep Subject >36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 >36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 >36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 >36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 >36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 >36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 >60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 >60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 >60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 >60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 >60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 >60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > > > ddaux=read.maimages(files=targets.micro$FileName,source="agilent", >+ >+ >other.columns=list(IsGeneDetected="gIsGeneDetected", >+ >+ >IsSaturated="gIsSaturated", >+ >+ >IsFeatNonUnifOF="gIsFeatNonUnifOL", >+ >+ >IsFeatPopnOL="gIsFeatPopnOL", >+ >+ >ChrCoord="chr_coord", >+ >+ >BGKmd="gBGMedianSignal", >+ >+ >BGKus="gBGUsed"), >+ >+ columns=list(Rf="gTotalGeneSignal", >+ >+ >Gf="gTotalProbeSignal", >+ >+ Rb="gMeanSignal", >+ >+ >Gb="gProcessedSignal"), >+ >+ verbose=TRUE,sep="\t",quote="") >Read 36_DMSO_1.txt >Read 36_DMSO_2.txt >Read 36_DMSO_3.txt >Read 36_TCDD_1.txt >Read 36_TCDD_2.txt >Read 36_TCDD_3.txt >Read 60_DMSO_1.txt >Read 60_DMSO_2.txt >Read 60_DMSO_3.txt >Read 60_TCDD_1.txt >Read 60_TCDD_2.txt >Read 60_TCDD_3.txt > > names(ddaux) >[1] >"R" "G" "Rb" "Gb" "targets" "genes" "source" "other" > > names(ddaux$genes) > [1] > "Row" "Col" "Start" > "Sequence" "ProbeUID" "ControlType" > [7] "ProbeName" "GeneName" "SystematicName" "Description" > > > dd=readMicroRnaAFE(targets.micro, verbose=TRUE) >Read 36_DMSO_1.txt >Read 36_DMSO_2.txt >Read 36_DMSO_3.txt >Read 36_TCDD_1.txt >Read 36_TCDD_2.txt >Read 36_TCDD_3.txt >Read 60_DMSO_1.txt >Read 60_DMSO_2.txt >Read 60_DMSO_3.txt >Read 60_TCDD_1.txt >Read 60_TCDD_2.txt >Read 60_TCDD_3.txt > > RGList: > dd$R: 'gTotalGeneSignal' > dd$G: 'gTotalProbeSignal' > dd$Rb: 'gMeanSignal' > dd$Gb: 'gProcessedSignal' > > > dd$genes=ddaux$genes[,c(6,7,8)] > > cvArray(dd, "MeanSignal", targets.micro, verbose=TRUE) >Foreground: MeanSignal > > FILTERING BY ControlType FLAG > > RAW DATA: 5335 > PROBES without CONTROLS: 4620 >---------------------------------- > (Non-CTRL) Unique Probe: 490 > (Non-CTRL) Unique Genes: 231 >---------------------------------- >DISTRIBUTION OF REPLICATED NonControl Probes >reps > 5 6 7 10 > 20 18 36 416 >------------------------------------------------------ >Replication at Probe level- MEDIAN CV >36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 >36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 60_TCDD_1 60_TCDD_2 > 0.078 0.081 0.091 0.081 > 0.077 0.067 0.076 0.066 0.103 0.073 0.086 >60_TCDD_3 > 0.069 >------------------------------------------------------ >DISTRIBUTION OF REPLICATED Noncontrol Genes >reps > 20 >231 >------------------------------------------------------ > > ddTGS.rma = rmaMicroRna(dd, normalize=TRUE, background=FALSE) >Calculating Expression > > ddPROC = filterMicroRna(ddTGS.rma, dd, > control = TRUE, IsGeneDetected = TRUE, > wellaboveNEG = FALSE, limIsGeneDetected = 50, > limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) >FILTERING PROBES BY FLAGS > > >FILTERING BY ControlType >Error in data.frame(as.character(PROBE_ID), >as.character(GENE_ID), as.character(probe.chr), : > arguments imply differing number of rows: 231, 0 > > > > >On Jun 2, 2010, at 5:32 AM, Pedro López Romero wrote: > > > Hi Neel, > > Try to use > readMicroRnaAFE(targets,verbose=TRUE) to load > your data into R instead of calling > read.maimages() by yourself. This will solve your problem > > > > Cheers > > > > p.- > > > > > > -----Mensaje original----- > > De: Neel Aluru [mailto:naluru@whoi.edu] > > Enviado el: Tuesday, June 01, 2010 7:34 PM > > Para: Martin Morgan > > CC: bioc; Pedro López Romero > > Asunto: Re: [BioC] AgiMicroRna - FilterMicroRna question > > > > Thanks, Martin. I have contacted Pedro today > and hopefully he will get a chance to see my > mail. In the mean time I will follow your suggestions. > > > > Thanks once again. > > > > Neel > > > > On Jun 1, 2010, at 1:31 PM, Martin Morgan wrote: > > > >> On 06/01/2010 06:43 AM, Neel Aluru wrote: > >>> Hello, > >>> > >>> I have asked this question before and > haven't heard from anyone. Sorry for reposting > it as I spent lot of time on it and still > cannot figure it out. I need to filter the data > before statistical analysis so as to remove the genes that are not detected. > > > >>> > >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, > >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, > >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) > >>> FILTERING PROBES BY FLAGS > >>> > >>> > >>> FILTERING BY ControlType > >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], > >>> ncol = dim(ddFILT)[2]) : > >>> attempt to set an attribute on NULL > >>> > >>> > >>> I checked my data files to see if the > required column (IsGeneDetected) is present and > it is there. But, for some reason it is not > detecting and I do not understand the error > message I am getting. If anyone can explain the > error message to me that would be great. I have posted the session info below. > >> > >> Hi Neel -- I can't help with specifics, but > >> > >>> matrix(NULL) > >> Error in matrix(NULL) : attempt to set an attribute on NULL > >> > >> so the proximate cause of the error message is likely that > >> ddFILT$other$gIsGeneDetected is equal to NULL, e.g., because it doesn't > >> exist. You can investigate this by inspecting the code, e.g., > >> > >>> options(error=browser()) > >> > >> and then re-running your code. See ?browser; when done use > >> options(error=NULL). Before that I'd revisit the help page for this > >> function and double-check that you are providing appropriate arguments. > >> > >> I've added > >> > >>> packageDescription('AgiMicroRna')$Maintainer > >> [1] "Pedro Lopez-Romero <plopez@cnic.es>" > >> > >> to the email, as Pedro in the best position to help you. > >> > >> Martin > >> > >>> Thank you very much, > >>> > >>> Neel > >>> > >>> > >>> > >>> > >>> Session Info > >>> > >>>> library("AgiMicroRna") > >>>> targets.micro=readTargets(infile="targets.txt", verbose=TRUE) > >>> > >>> Target File > >>> FileName Treatment GErep Subject > >>> 36_DMSO_1 36_DMSO_1.txt 36DMSO 1 1 > >>> 36_DMSO_2 36_DMSO_2.txt 36DMSO 1 2 > >>> 36_DMSO_3 36_DMSO_3.txt 36DMSO 1 3 > >>> 36_TCDD_1 36_TCDD_1.txt 36TCDD 2 1 > >>> 36_TCDD_2 36_TCDD_2.txt 36TCDD 2 2 > >>> 36_TCDD_3 36_TCDD_3.txt 36TCDD 2 3 > >>> 60_DMSO_1 60_DMSO_1.txt 60DMSO 3 1 > >>> 60_DMSO_2 60_DMSO_2.txt 60DMSO 3 2 > >>> 60_DMSO_3 60_DMSO_3.txt 60DMSO 3 3 > >>> 60_TCDD_1 60_TCDD_1.txt 60TCDD 4 1 > >>> 60_TCDD_2 60_TCDD_2.txt 60TCDD 4 2 > >>> 60_TCDD_3 60_TCDD_3.txt 60TCDD 4 3 > >>> > >>>> dd.micro=read.maimages(targets.micro$FileName, > >>> columns=list(R="gTotalGeneSignal",G= > >>> "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), > >>> > annotation=c("ProbeUID","ControlType","ProbeName","GeneName","System aticName", > >>> "sequence", "accessions","probe_mappings", > >>> "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", > >>> "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) > >>> Read 36_DMSO_1.txt > >>> Read 36_DMSO_2.txt > >>> Read 36_DMSO_3.txt > >>> Read 36_TCDD_1.txt > >>> Read 36_TCDD_2.txt > >>> Read 36_TCDD_3.txt > >>> Read 60_DMSO_1.txt > >>> Read 60_DMSO_2.txt > >>> Read 60_DMSO_3.txt > >>> Read 60_TCDD_1.txt > >>> Read 60_TCDD_2.txt > >>> Read 60_TCDD_3.txt > >>>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE) > >>> Foreground: MeanSignal > >>> > >>> FILTERING BY ControlType FLAG > >>> > >>> RAW DATA: 5335 > >>> PROBES without CONTROLS: 4620 > >>> ---------------------------------- > >>> (Non-CTRL) Unique Probe: 490 > >>> (Non-CTRL) Unique Genes: 231 > >>> ---------------------------------- > >>> DISTRIBUTION OF REPLICATED NonControl Probes > >>> reps > >>> 5 6 7 10 > >>> 20 18 36 416 > >>> ------------------------------------------------------ > >>> Replication at Probe level- MEDIAN CV > >>> 36_DMSO_1 36_DMSO_2 36_DMSO_3 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 > >>> 60_DMSO_2 60_DMSO_3 > >>> 0.078 0.081 0.091 0.081 0.077 0.067 > >>> 0.076 0.066 0.103 > >>> 60_TCDD_1 60_TCDD_2 60_TCDD_3 > >>> 0.073 0.086 0.069 > >>> ------------------------------------------------------ > >>> DISTRIBUTION OF REPLICATED Noncontrol Genes > >>> reps > >>> 20 > >>> 231 > >>> ------------------------------------------------------ > >>>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE) > >>> Calculating Expression > >>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE, > >>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50, > >>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) > >>> FILTERING PROBES BY FLAGS > >>> > >>> > >>> FILTERING BY ControlType > >>> Error in matrix(ddFILT$other$gIsGeneDetected, nrow = dim(ddFILT)[1], > >>> ncol = dim(ddFILT)[2]) : > >>> attempt to set an attribute on NULL > >>> > >>>> MMM = ddTGS.rma$Rb > >>>> colnames(MMM) = colnames(dd.micro$Rb) > >>>> maintitle='TGS.rma' > >>>> colorfill='blue' > >>>> ddaux=ddTGS.rma > >>>> ddaux$G=MMM > >>>> mvaMicroRna(ddaux, maintitle, verbose=TRUE) > >>> > >>> ------------------------------------------------------ > >>> mvaMicroRna info: > >>> FEATURES : 231 > >>> POSITIVE CTRL: 12 > >>> NEGATIVE CTRL: 7 > >>> STRUCTURAL: 3 > >>>> rm(ddaux) > >>>> RleMicroRna(MMM,"RLE TGS.rma", colorfill) > >>>> boxplotMicroRna(MMM, maintitle, colorfill) > >>>> plotDensityMicroRna(MMM, maintitle) > >>>> spottypes = readSpotTypes() > >>>> ddTGS.rma$genes$Status = controlStatus(spottypes, ddTGS.rma) > >>> Matching patterns for: ProbeName GeneName > >>> Found 231 gene > >>> Found 1 BLANK > >>> Found 1 Blank > >>> Found 0 blank > >>> Found 6 positive > >>> Found 0 negative > >>> Found 0 flag1 > >>> Found 0 flag2 > >>> Found 6 flag3 > >>> Found 5 flag4 > >>> Found 1 flag5 > >>> Setting attributes: values > >>>> i = ddTGS.rma$genes$Status == "gene" > >>>> esetPROC = esetMicroRna(ddTGS.rma[i,], targets.micro, > >>> makePLOT=TRUE, verbose = TRUE) > >>> outPUT DATA: esetPROC > >>> Features Samples > >>> 231 12 > >>>> design=model.matrix(~-1+treatment) > >>>> print(design) > >>> treatment36DMSO treatment36TCDD treatment60DMSO treatment60TCDD > >>> 1 1 0 0 0 > >>> 2 1 0 0 0 > >>> 3 1 0 0 0 > >>> 4 0 1 0 0 > >>> 5 0 1 0 0 > >>> 6 0 1 0 0 > >>> 7 0 0 1 0 > >>> 8 0 0 1 0 > >>> 9 0 0 1 0 > >>> 10 0 0 0 1 > >>> 11 0 0 0 1 > >>> 12 0 0 0 1 > >>> attr(,"assign") > >>> [1] 1 1 1 1 > >>> attr(,"contrasts") > >>> attr(,"contrasts")$treatment > >>> [1] "contr.treatment" > >>> > >>>> fit=lmFit(esetPROC, design) > >>>> cont.matrix = makeContrasts(treatment36TCDDvstreatment36DMSO = > >>> treatment36TCDD-treatment36DMSO, treatment60TCDDvstreatment60DMSO = > >>> treatment60TCDD-treatment60DMSO,treatment60TCDDvstreatment36TCDD = > >>> treatment60TCDD-treatment36TCDD, treatment60DMSOvstreatment36DMSO = > >>> treatment60DMSO-treatment36DMSO, levels=design) > >>>> print(cont.matrix) > >>> Contrasts > >>> Levels treatment36TCDDvstreatment36DMSO > >>> treatment60TCDDvstreatment60DMSO > >>> treatment36DMSO -1 > >>> 0 > >>> treatment36TCDD 1 > >>> 0 > >>> treatment60DMSO 0 > >>> -1 > >>> treatment60TCDD 0 > >>> 1 > >>> Contrasts > >>> Levels treatment60TCDDvstreatment36TCDD > >>> treatment60DMSOvstreatment36DMSO > >>> treatment36DMSO 0 > >>> -1 > >>> treatment36TCDD -1 > >>> 0 > >>> treatment60DMSO 0 > >>> 1 > >>> treatment60TCDD 1 > >>> 0 > >>>> fit2 = contrasts.fit(fit,cont.matrix) > >>>> print(head(fit2$coeff)) > >>> Contrasts > >>> treatment36TCDDvstreatment36DMSO > treatment60TCDDvstreatment60DMSO > >>> > dre-let-7a 0.038640984 0.013333873 > >>> > dre-let-7b 0.074038749 -0.031608286 > >>> > dre-let-7c 0.026244357 -0.005682488 > >>> > dre-let-7d 0.067340768 0.055567054 > >>> > dre-let-7e 0.004569306 0.136348664 > >>> > dre-let-7f 0.042880109 0.085568058 > >>> Contrasts > >>> treatment60TCDDvstreatment36TCDD > treatment60DMSOvstreatment36DMSO > >>> > dre-let-7a 1.7358343 1.76114142 > >>> > dre-let-7b 0.1366920 0.24233899 > >>> > dre-let-7c 0.9920976 1.02402449 > >>> > dre-let-7d 0.8098432 0.82161694 > >>> > dre-let-7e 0.1186829 -0.01309647 > >>> > dre-let-7f 1.1245878 1.08189990 > >>>> fit2 = eBayes(fit2) > >>>> fit2 = basicLimma(esetPROC, design, cont.matrix, verbose = TRUE) > >>> DATA > >>> Features Samples > >>> 231 12 > >>> > >>>> DE = getDecideTests(fit2, DEmethod = "separate", MTestmethod = > >>> "BH", PVcut = 0.1, verbose = TRUE) > >>> > >>> ------------------------------------------------------ > >>> Method for Selecting DEGs: separate > >>> Multiple Testing method: BH - pval 0.1 > >>> > >>> treatment36TCDDvstreatment36DMSO treatment60TCDDvstreatment60DMSO > >>> UP 0 5 > >>> DOWN 0 1 > >>> treatment60TCDDvstreatment36TCDD treatment60DMSOvstreatment36DMSO > >>> UP 56 51 > >>> DOWN 80 91 > >>> ------------------------------------------------------ > >>>> pvalHistogram(fit2, DE, PVcut = 0.1, DEmethod ="separate", > >>> MTestmethod="BH",cont.matrix, verbose= TRUE) > >>>> significantMicroRna(esetPROC, ddTGS.rma, targets.micro, fit2, > >>> cont.matrix, DE, DEmethod = "separate", MTestmethod= "BH", PVcut = > >>> 0.1, Mcut=0, verbose=TRUE) > >>> ------------------------------------------------------ > >>> CONTRAST: 1 - treatment36TCDDvstreatment36DMSO > >>> > >>> Error in data.frame(PROBE_ID, as.character(GENE_ID), > >>> as.character(chr_coord), : > >>> arguments imply differing number of rows: 231, 0 > >>> > >>> > >>> > >>> > >>> Neel Aluru > >>> Postdoctoral Scholar > >>> Biology Department > >>> Woods Hole Oceanographic Institution > >>> Woods Hole, MA 02543 > >>> USA > >>> 508-289-3607 > >>> > >>> _______________________________________________ > >>> Bioconductor mailing list > >>> Bioconductor@stat.math.ethz.ch > >>> https://stat.ethz.ch/mailman/listinfo/bioconductor > >>> Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > >> > >> > >> -- > >> Martin Morgan > >> Computational Biology / Fred Hutchinson Cancer Research Center > >> 1100 Fairview Ave. N. > >> PO Box 19024 Seattle, WA 98109 > >> > >> Location: Arnold Building M1 B861 > >> Phone: (206) 667-2793 > >> > > > > Neel Aluru > > Postdoctoral Scholar > > Biology Department > > Woods Hole Oceanographic Institution > > Woods Hole, MA 02543 > > USA > > 508-289-3607 > > > > > > > > > > *************** AVISO LEGAL *************** > > Este mensaje va dirigido, de manera exclusiva, a su destinatario y > > contiene información confidencial y sujeta al secreto profesional, > > cuya divulgación no está permitida por la ley. En caso de haber > > recibido este mensaje por error, le rogamos que, de forma inmediata, > > nos lo comunique mediante correo electrónico remitido a nuestra > > atención o a través del teléfono (+34 914531200) y proceda a su > > eliminación, así como a la de cualquier documento adjunto al mismo. > > Asimismo, le comunicamos que la distribución, copia o utilización de > > este mensaje, o de cualquier documento adjunto al mismo, cualquiera > > que fuera su finalidad, están prohibidas por la ley. Le informamos, > > como destinatario de este mensaje, que el correo electrónico y las > > comunicaciones por medio de Internet no permiten asegurar ni > > garantizar la confidencialidad de los mensajes transmitidos, así como > > tampoco su integridad o su correcta recepción, por lo que el CNIC no > > asume responsabilidad alguna por tales circunstancias. Si no > > consintiese la utilización del correo electrónico o de las > > comunicaciones vía Internet le rogamos nos lo comunique y ponga en > > nuestro conocimiento de manera inmediata. > > > > *************** LEGAL NOTICE ************** > > This message is intended exclusively for the person to whom it is > > addressed and contains privileged and confidential information > > protected from disclosure by law. If you are not the addressee > > indicated in this message, you should immediately delete it and any > > attachments and notify the sender by reply e-mail or by phone > > (+34 914531200). In such case, you are hereby notified that any > > dissemination, distribution, copying or use of this message or any > > attachments, for any purpose, is strictly prohibited by law. We > > hereby inform you, as addressee of this message, that e-mail and > > Internet do not guarantee the confidentiality, nor the completeness > > or proper reception of the messages sent and, thus, CNIC does not > > assume any liability for those circumstances. Should you not agree > > to the use of e-mail or to communications via Internet, you are > > kindly requested to notify us immediately. > > > >Neel Aluru >Postdoctoral Scholar >Biology Department >Woods Hole Oceanographic Institution >Woods Hole, MA 02543 >USA >508-289-3607 > > > > >*************** AVISO LEGAL *************** >Este mensaje va dirigido, de manera exclusiva, a su destinatario y >contiene información confidencial y sujeta al secreto profesional, >cuya divulgación no está permitida por la ley. En caso de haber >recibido este mensaje por error, le rogamos que, de forma inmediata, >nos lo comunique mediante correo electrónico remitido a nuestra >atención o a través del teléfono (+34 914531200) y proceda a su >eliminación, así como a la de cualquier documento adjunto al mismo. >Asimismo, le comunicamos que la distribución, copia o utilización de >este mensaje, o de cualquier documento adjunto al mismo, cualquiera >que fuera su finalidad, están prohibidas por la ley. Le informamos, >como destinatario de este mensaje, que el correo electrónico y las >comunicaciones por medio de Internet no permiten asegurar ni >garantizar la confidencialidad de los mensajes transmitidos, así como >tampoco su integridad o su correcta recepción, por lo que el CNIC no >asume responsabilidad alguna por tales circunstancias. Si no >consintiese la utilización del correo electrónico o de las >comunicaciones vía Internet le rogamos nos lo comunique y ponga en >nuestro conocimiento de manera inmediata. > >*************** LEGAL NOTICE ************** >This message is intended exclusively for the person to whom it is >addressed and contains privileged and confidential information >protected from disclosure by law. If you are not the addressee >indicated in this message, you should immediately delete it and any >attachments and notify the sender by reply e-mail or by phone >(+34 914531200). In such case, you are hereby notified that any >dissemination, distribution, copying or use of this message or any >attachments, for any purpose, is strictly prohibited by law. We >hereby inform you, as addressee of this message, that e-mail and >Internet do not guarantee the confidentiality, nor the completeness >or proper reception of the messages sent and, thus, CNIC does not >assume any liability for those circumstances. Should you not agree >to the use of e-mail or to communications via Internet, you are >kindly requested to notify us immediately. Neel Aluru Ph.D. Post doctoral Scholar Biology Department Redfield 304 (MS#32) Woods Hole Oceanographic Institution Woods Hole MA 02543 USA Phone: (508) 289-3607 [Office] 774-392-3727 [Cell] RID: A-7237-2009 [[alternative HTML version deleted]]
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