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  • DESeq2


    An announcement of interest to users of DESeq:

    Mike Love, Wolfgang Huber and I have been updating the DESeq package. This resulted in the package DESeq2, which is already now available from the Bioconductor development branch, and scheduled to be included in the next Bioconductor release.

    For several release cycles, the original package (DESeq) will be maintained at its current functionality, in order to not disrupt the workflows of DESeq users. For new projects, we recommend using DESeq2. Major innovations are:

    * Base class: SummarizedExperiment (from the GenomicRanges package) is used as the superclass for storing the data, rather than eSet. This allows closer integration with upstream workflows involving GenomicRanges features, such as summarizeOverlaps, and facilitates downstream analyses of the genomic regions of interest.

    * Simplified workflow: the wrapper function DESeq() performs all steps for a differential expression analysis. The individual steps are of course also accessible.

    * More powerful statistics: incorporation of prior distributions into the estimation of dispersions and fold changes (empirical-Bayes shrinkage). The dispersion shrinkage improves power compared to the old DESeq. The fold changes shrinkage help moderate the otherwise large spread in log fold changes for genes with low counts, while it has negligible effect on genes with high counts; it may be particularly useful for visualisation, clustering, classification, ordination (PCA, MDS), similar to the variance-stabilizing transformation in the old DESeq. A Wald test for significance is provided as the default inference method, with the chi-squared test of the previous version is also available. A manuscript is in preparation.

    * Normalization: it is possible to provide a matrix of sample- and gene-specific normalization factors, which allows the use of normalisation factors from Bioconductor packages such as cqn and EDASeq.

    Examples of usage are provided in the vignette, and more details are available in the manual pages (specifically, the DESeq function and estimateDispersions function).

    Enjoy -

    Mike, Simon, Wolfgang.

  • #2
    Exciting news, thanks Simon. You guys have created some of the best tools out there and I am excited to see what this offers.

    PS. I notice that the vignette is as well written as your last and puts the details on a level that people like me can easily grasp. Thanks.
    Last edited by chadn737; 03-13-2013, 10:36 AM.


    • #3
      Great news, thanks Simon, Mike, Wolfgang. Looking forward to going through the vignette. Are you publishing any comparisons with the original DESeq and/or other tools?


      • #4
        Thanks! Looking forward to the new options.

        Is it just me or the bioconductor linked vignette is a 16Mb pdf with 10701 pages and a lot of repetitions starting from page 6?


        • #5
          Originally posted by EGrassi View Post
          Is it just me or the bioconductor linked vignette is a 16Mb pdf with 10701 pages and a lot of repetitions starting from page 6?
          Yes, that's a bug that Mike has already fixed. The corrected vignette should become available today or tomorrow.


          • #6

            I'd love to install deseq2. But I have a problem in installing in.

            > source("")
            BioC_mirror =
            Change using chooseBioCmirror().
            > biocLite("DESeq2")
            Using R version 2.11.1, biocinstall version 2.6.10.
            Installing Bioconductor version 2.6 packages:
            [1] "DESeq2"
            Please wait...

            Warning message:
            In getDependencies(pkgs, dependencies, available, lib) :
            package ‘DESeq2’ is not available

            I have no problem in installing deseq though.

            Thanks a lot!



            • #7
              I would first try making sure R is up to date.


              • #8
                oh, it is a R package from the core installation.
                R version 2.11.1, biocinstall version 2.6.10.



                • #9
                  If you have an old R, the BiocLite script will pull matching old Bioconductor packages. DESeq2 is new, and while you could install it manually on an old R, the recommended way is to use R 3.0.0, and then biocLite will find and install it automatically.


                  • #10


                    • #11
                      As said above, you guys do great things. Question; do you believe that this package would be good for determining differentially methylated regions from enrichment data (ie, MBD-seq or MeDIP-seq)? I know there are some papers using DESeq for this purpose, so I imagine so, but I was curious if you have any specific thoughts or caveats in mind.


                      • #12

                        Does deseq development version better than the release version? The manual that i found on Bioconductor seems to aim for the development version since the "normalize=True" does not work. I am just wondering whether it is worthwhile to switch to a development version and where to find it?



                        • #13
                          This is my DESeqDataSet object
                          > Genes
                          class: DESeqDataSet
                          dim: 21937 4
                          assays(1): counts
                          rownames(21937): 0610007C21Rik 0610007L01Rik ... Zzef1 Zzz3
                          rowData metadata column names(0):
                          colnames(4): 13-5.bam 13-5-5.bam E15-2.sorted.bam E15-5.sorted.bam
                          colData names(2): fileName E13E15
                          Since I want to test only "13-5.bam" and "13-5-5.bam", so I set colData(Genes)$E13E15 like this:
                          >colData(Genes)$E13E15 <- factor(colData(Genes)$E13E15,levels=c("E13WT","E13KO"))
                          > colData(Genes)
                          DataFrame with 4 rows and 2 columns
                          fileName E13E15
                          <BamFileList> <factor>
                          13-5.bam ######## E13WT
                          13-5-5.bam ######## E13KO
                          E15-2.sorted.bam ######## NA
                          E15-5.sorted.bam ######## NA
                          However, when I change the DESeqDataSet object into Deseq object, it reports error:
                          > Genes<-DESeq(Genes)
                          estimating size factors
                          estimating dispersions
                          same number of samples and coefficients to fit, estimating dispersion by treating samples as replicates
                          gene-wise dispersion estimates
                          mean-dispersion relationship
                          final dispersion estimates
                          fitting generalized linear model

                          error: element-wise multiplication: incompatible matrix dimensions: 4x1 and 2x1

                          Last edited by trpc; 07-25-2013, 10:37 AM.


                          • #14
                            Dear Simon and everyone,

                            I have downloaded DESeq2 package with no trouble. But when I tried to run your example R code, I got trouble even with the first line "dds <- DESeqDataSet(se = se, design = ~ condition)". The error message is "error in evaluating the argument 'x' in selecting a method for function 'assays': Error: object 'se' not found".

                            I have run "library(DESeq2)" at the very beginning. I wonder what else I should do to make it work?

                            I used R-3.0.1 for the installation.

                            Thanks in advance.



                            • #15
                              Which example code are your running, i.e., what documentation are you reading?


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