Transcriptome sequencing (RNA-Seq) has become a key technology in transcriptome studies because it can quantify overall expression levels and the degree of alternative splicing for each gene simultaneously. Many methods and tools, including quite a few R / Bioconductor packages, have been developed to deal with RNA-Seq data for differential expression analysis and thereafter functional analysis aiming at novel biological and biomedical discoveries. However, those tools mainly focus on each gene’s overall expression and may miss the opportunities for discoveries regarding alternative splicing or the combination of the two.
SeqGSEA is novel R / Bioconductor package to derive biological insight by integrating differential expression (DE) and differential splicing (DS) from RNA-Seq data with functional gene set analysis. Due to the digital feature of RNA-Seq count data, the package utilizes negative binomial distributions for statistical modeling to first score differential expression and splicing in each gene, respectively.
Then, integration strategies are applied to combine the two scores for integrated gene set enrichment analysis. See the publication Wang and Cairns (2013) for more details. The SeqGSEA package can also give detection results of differentially expressed genes and differentially spliced genes based on sample label permutation.
Availability – The package can be accessed at the URL: http://bioconductor.org/packages/rel…l/SeqGSEA.html
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