DESeq2 is implemented as a package for the R statistical environment and is available [10 ] as part of the Bioconductor project [11 (link)]. The count matrix and metadata, including the gene model and sample information, are stored in an S4 class derived from the SummarizedExperiment class of the GenomicRanges package [60 (link)]. SummarizedExperiment objects containing count matrices can be easily generated using the summarizeOverlaps function of the GenomicAlignments package [61 ]. This workflow automatically stores the gene model as metadata and additionally other information such as the genome and gene annotation versions. Other methods to obtain count matrices include the htseq-count script [62 (link)] and the Bioconductor packages easyRNASeq [63 (link)] and featureCount [64 (link)].
The DESeq2 package comes with a detailed vignette, which works through a number of example differential expression analyses on real datasets, and the use of the rlog transformation for quality assessment and visualization. A single function, called DESeq, is used to run the default analysis, while lower-level functions are also available for advanced users.
The DESeq2 package comes with a detailed vignette, which works through a number of example differential expression analyses on real datasets, and the use of the rlog transformation for quality assessment and visualization. A single function, called DESeq, is used to run the default analysis, while lower-level functions are also available for advanced users.
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