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  • Richard Barker
    replied
    Wow, thats a lot of replicates, so your initial table has 120 columns? And in the first row 44 of them just have A, 23 have B and 55 are C?
    I've heard of an experiment with so many rep's before so i'm trying to imagine the table structure.

    Leave a comment:


  • pinki999
    replied
    Hi,

    A, B and C are the conditions and the numbers represent the replicates.

    Leave a comment:


  • Richard Barker
    replied
    Could you explain what the numbers and letters refer to in the following line?

    condition = factor(c(rep("A", 44), rep("B", 21), rep("C", 55)))

    I

    Leave a comment:


  • pinki999
    replied
    Hi,

    I used the code according to the vigenette:

    countTable = read.table("count_data.txt", header=TRUE, row.names=1)
    condition = factor(c(rep("A", 44), rep("B", 21), rep("C", 55)))
    cds = newCountDataSet( countTable, condition )
    cds = estimateSizeFactors( cds )
    cdsBlind = estimateDispersions( cds, method="blind" )
    vsd = varianceStabilizingTransformation( cdsBlind )

    I am also attaching the meanSdPlot. First plot is from log transformed data and the second is from VST.

    thanks,
    Attached Files

    Leave a comment:


  • Richard Barker
    replied
    Can you share the code/script you used as it might make the issue a bit clearer?

    Leave a comment:


  • DESeq, elevated SD in lower count range even after VST

    Hi,

    I am using DESeq for differential expression analysis. When I transformed the data for variance stabilization using varianceStabilizingTransformation from DESeq, I still see an elevated standard deviation in the lower count range. Does anyone have come across similar issue? I am using appox. 50 vs 50 replicates. Does large number of replicates creates such elevation?..



    Thanks

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