[BioC] AgiMicroRna - FilterMicroRna question

James W. MacDonald jmacdon at med.umich.edu
Tue Jun 1 16:39:16 CEST 2010


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
> 
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-- 
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
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