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  • using DESeq2 for RNA-Seq analysis

    Hello All,
    i have used the DESeq package for RNA-Seq analysis up until recently.
    after discovering the DESeq2 package, i have tried using it for that kind of analysis, and encountered some problems.
    1. i have read that the package offers two different statistical testing for differential expression : the "nbinomWalsTest" and the "nbinomLRT", i can't seem to understand fully what is the difference between them and what is the recomanded test using for RNA-Seq analysis.
    2. my current data contains 4 conditions, one of them is the control and the other three ate different treatments. when using the nbinomWaldTest i have used the "contrast" option in order to define the specific comparison that i am intreasted in. is there some kind of option like that for the LRT test? since right now i only receive results comparing all conditions to the control.
    3. i would like to receive MA plots for my comparisons between different conditions, right now all i was able to receive is the plots of comparing each condition to the control (since those are the results available in the results() function by default)

    i general, if anyone has expreience using this tool on RNA-Seq analysis, i would really appritiate any help regarding the steps to take for receiving full results.

    thank you very much,
    olga.

  • #2
    Dear Olga,

    Did you hear back from someone ?
    I'm also interested in the answers of your questions...

    Comment


    • #3
      No, actually i did not hear back from anyone.
      I am using the Wald test for the analysis at this point.
      If i will recieve any information regarding those issues i will post the answers here.

      Comment


      • #4
        Olga, Mariana

        sorry for not responding earlier, not sure why this slipped through (it might also be useful to ask questions about Bioconductor packages on the Bioconductor mailing list http://www.bioconductor.org/help/mailing-list/ first).

        A lot has changed (improved) in the user interface and documentation of the DESeq2 package since October, so I suggest you have a look at the latest version (http://www.bioconductor.org/packages...ml/DESeq2.html ) and the vignette Analyzing RNA-Seq data with the "DESeq2" package. Also, we posted a preprint of the DESeq2 paper: http://biorxiv.org/content/early/2014/02/19/002832
        Let us then know when questions remain!

        Kind regards
        Wolfgang
        Last edited by Wolfgang Huber; 02-20-2014, 07:16 AM. Reason: Typo fix
        Wolfgang Huber
        EMBL

        Comment


        • #5
          Dear Wolfgang,

          I am trying to use DESeq2 as I have seen many changes from the previous version. I am having a lot of trouble following the data (raw read counts) input part.
          In the vignette it mentions - " An example of the steps to produce a SummarizedExperiment can be found in the data package parathyroidSE,", but the parathyroidSE link seems non functional.
          Could you please fix this error or direct me to a source where I can get help getting started with DESeq2.

          Thanks
          Priya

          Comment


          • #6
            Just to note that the conversation with Olga continued on the Bioc mailing list:



            Priya, the link is fixed in the development version, which will be released in < 1 month.

            the link should have been:

            This package provides RangedSummarizedExperiment objects of read counts in genes and exonic parts for paired-end RNA-Seq data from experiments on primary cultures of parathyroid tumors. The data were presented in the article


            We have created another vignette for DESeq2 version 1.3 which goes slower, starting with how to count reads in genes. This should appear here in 1-2 days:

            Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

            Comment


            • #7
              Awesome !! I appreciate your efforts !!

              Thanks again
              Priya

              Comment

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