pairedGSEA

pairedGSEA is an R package that helps you to run a paired differential gene expression (DGE) and splicing (DGS) analysis. Providing a bulk RNA count data, pairedGSEA combines the results of DESeq2 (DGE) and DEXSeq (DGS), aggregates the p-values to gene level, and allows you to run a subsequent gene set over-representation analysis using its implementation of the fgsea::fora function.

Article

pairedGSEA is published in BMC Biology.

Please cite with citation("pairedGSEA")

Installation

Dependencies

# Install Bioconductor dependencies
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("SummarizedExperiment", "S4Vectors", "DESeq2", "DEXSeq", "fgsea", "sva", "BiocParallel"))

Install pairedGSEA from Bioconductor

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("pairedGSEA")

Install development version from GitHub

# Install pairedGSEA from github
devtools::install_github("shdam/pairedGSEA", build_vignettes = TRUE)

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("pairedGSEA")

Interoperability with IsoformSwitchAnalyzeR

IsoformSwitchAnalyzeR identifies, annotates, and visualizes Isoform Switches with Functional Consequences (from RNA-seq data).

Import and export between the packages with:

• IsoformSwitchAnalyzeR::importPairedGSEA()
• IsoformSwitchAnalyzeR::exportToPairedGSEA()

Usage

Please see the User Guide vignette for a detailed description of usage.

Here is a quick run-through of the functions:

Load example data.

suppressPackageStartupMessages(library("SummarizedExperiment"))
library("pairedGSEA")

data("example_se")
example_se
#> class: SummarizedExperiment 
#> dim: 5611 6 
#> metadata(0):
#> assays(1): counts
#> rownames(5611): ENSG00000282880:ENST00000635453
#>   ENSG00000282880:ENST00000635195 ... ENSG00000249230:ENST00000504393
#>   ENSG00000249244:ENST00000505994
#> rowData names(0):
#> colnames(6): GSM1499784 GSM1499785 ... GSM1499791 GSM1499792
#> colData names(5): study id source final_description group_nr

Run paired differential analysis

set.seed(500) # For reproducible results

diff_results <- paired_diff(
  example_se,
  group_col = "group_nr",
  sample_col = "id",
  baseline = 1,
  case = 2,
  store_results = FALSE,
  quiet = TRUE
  )
#> No significant surrogate variables
#> converting counts to integer mode
#> Warning in DESeqDataSet(rse, design, ignoreRank = TRUE): some variables in
#> design formula are characters, converting to factors

Over-representation analysis of results

# Define gene sets in your preferred way
gene_sets <- pairedGSEA::prepare_msigdb(
    species = "Homo sapiens",
    db_species = "HS",
    collection = "C5",
    gene_id_type = "ensembl_gene"
    )

ora <- paired_ora(
  paired_diff_result = diff_results,
  gene_sets = gene_sets
  )
#> Running over-representation analyses
#> Joining result

You can now plot the enrichment scores against each other and identify pathways of interest.

plot_ora(
    ora, 
    paired = FALSE # Available in version 1.1.0 and newer
    ) +
    ggplot2::theme_classic()

Report issues

If you have any issues or questions regarding the use of pairedGSEA, please do not hesitate to raise an issue on GitHub. In this way, others may also benefit from the answers and discussions.