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  • Nelala
    Junior Member
    • Oct 2013
    • 1

    #1

    DESeDESeq2: How to turn off Cook’s distance testing?

    Dear DESeq2 users,

    I’m completely new to transcriptome analyses, and I’ve neither used R or DESeq2 before, so I apologize for the probably basic question...

    I luckily managed to install & run the DESeq2 package with my own samples by using the "default" settings from the vignette.
    Now I’d like to turn off Cook’s distance testing (by using the argument “cooksCutoff=FALSE”), but here is where I’m stuck: no matter what I try I get the error “unused argument (cooksCutoff = FALSE)”.

    Here’s the script I’m using for the “default” analysis of my samples:

    # Counts-Matrix
    CountTableAC <- read.table("CountTableAC.txt", header=TRUE, row.names=1)
    head(CountTableAC)

    # column information as data.frame
    DesignAC <- data.frame(
    row.names = colnames( CountTableAC ),
    condition = c( "A", "A", "C", "C" ) )
    DesignAC


    dds <- DESeqDataSetFromMatrix(countData = CountTableAC,
    colData = DesignAC,
    design = ~ condition)
    colData(dds)$condition <- factor(colData(dds)$condition,
    levels=c("A","C" ))

    dds

    #Differential Expression Analysis:
    dds <- DESeq(dds)
    res <- results(dds)
    res <- res[order(res$padj),]
    head(res)

    plotMA(dds,ylim=c(-2,2),main="DESeq2")

    Can anyone please tell me exactly where and what to change to disable Cook’s testing?

    Thanks a lot for any advice!
    Nelala
  • Michael Love
    Senior Member
    • Jul 2013
    • 333

    #2
    cooksCutoff moved from DESeq() to results() when you upgraded to Bioc 2.13. You can turn it off there.

    Big changes like this are documented in the NEWS file. You can access this from R like this:

    news(Version >= "1.1", package="DESeq2")

    then you would have seen:

    Changes in version 1.1.31:

    o The adjustment of p-values and use of Cook's distance
    for outlier detection is moved to results() function
    instead of nbinomWaldTest(), nbinomLRT(), or DESeq().
    This allows the user to change parameter settings
    without having to refit the model.

    Also, you should check the vignette of the current package version, instead of the one on the web:

    vignette("DESeq2")

    You can see what vignettes are available (for DESeq2 there is only the one above):

    browseVignettes(package="DESeq2")

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