[BioC] AgiMicroRna - FilterMicroRna question
Neel Aluru
naluru at whoi.edu
Tue Jun 1 16:43:18 CEST 2010
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$FileName,
>> columns=list(R="gTotalGeneSignal",G= "gTotalProbeSignal",Rb="gMeanSignal", Gb="gProcessedSignal"), annotation=c("ProbeUID","ControlType","ProbeName","GeneName","SystematicName",
>> "sequence", "accessions","probe_mappings", "gIsGeneDetected","gIsSaturated","gIsFeatNonUnifOL", "gIsFeatPopnOL","chr_coord","gBGMedianSignal","gBGUsed")) Read
>> 36_DMSO_1.txt Read 36_DMSO_2.txt Read 36_DMSO_3.txt Read
>> 36_TCDD_1.txt Read 36_TCDD_2.txt Read 36_TCDD_3.txt Read
>> 60_DMSO_1.txt Read 60_DMSO_2.txt Read 60_DMSO_3.txt Read
>> 60_TCDD_1.txt Read 60_TCDD_2.txt Read 60_TCDD_3.txt
>>> cvArray(dd.micro, "MeanSignal", targets.micro, verbose=TRUE)
>> Foreground: MeanSignal
>> FILTERING BY ControlType FLAG
>> RAW DATA: 5335 PROBES without CONTROLS:
>> 4620 ---------------------------------- (Non-CTRL) Unique Probe: 490
>> (Non-CTRL) Unique Genes: 231 ---------------------------------- DISTRIBUTION OF REPLICATED NonControl Probes reps 5 6 7 10 20
>> 18 36 416 ------------------------------------------------------ Replication at Probe level- MEDIAN CV 36_DMSO_1 36_DMSO_2 36_DMSO_3
>> 36_TCDD_1 36_TCDD_2 36_TCDD_3 60_DMSO_1 60_DMSO_2 60_DMSO_3 0.078
>> 0.081 0.091 0.081 0.077 0.067 0.076 0.066
>> 0.103 60_TCDD_1 60_TCDD_2 60_TCDD_3 0.073 0.086 0.069 ------------------------------------------------------ DISTRIBUTION
>> OF REPLICATED Noncontrol Genes reps 20 231 ------------------------------------------------------
>>> ddTGS.rma = rmaMicroRna(dd.micro, normalize=TRUE, background=FALSE)
>> Calculating Expression
>>> ddPROC = filterMicroRna(ddTGS.rma, dd.micro, control = TRUE,
>> IsGeneDetected = TRUE, wellaboveNEG = FALSE, limIsGeneDetected = 50,
>> limNEG = 25, makePLOT = FALSE, targets.micro, verbose = TRUE) FILTERING PROBES BY FLAGS
>> FILTERING BY ControlType Error in
>> matrix(ddFILT$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
More information about the Bioconductor
mailing list