[BioC] AgiMicroRna - FilterMicroRna question
Neel Aluru
naluru at whoi.edu
Tue Jun 1 19:33:59 CEST 2010
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","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
>>
>> _______________________________________________
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>
>
> --
> 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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