fgsea

An R-package for fast preranked gene set enrichment analysis (GSEA). The package implements a special algorithm to calculate the empirical enrichment score null distributions simulthaneously for all the gene set sizes, which allows up to several hundred times faster execution time compared to original Broad implementation. See the preprint for algorithmic details.

Full vignette can be found here: http://bioconductor.org/packages/devel/bioc/vignettes/fgsea/inst/doc/fgsea-tutorial.html

Installation

library(devtools)
install_github("ctlab/fgsea")

To install fgsea on R 3.2, use r-3.2 branch:

install_github("ctlab/fgsea", ref="r-3.2")

Quick run

Loading libraries

library(data.table)
library(fgsea)
library(ggplot2)

Loading example pathways and gene-level statistics:

data(examplePathways)
data(exampleRanks)

Running fgsea (should take about 10 seconds):

fgseaRes <- fgsea(pathways = examplePathways, 
                  stats = exampleRanks,
                  minSize=15,
                  maxSize=500,
                  nperm=10000)

The head of resulting table sorted by p-value:

                                    pathway         pval        padj        ES      NES nMoreExtreme size
 1:                      5990980_Cell_Cycle 0.0001221001 0.002356366 0.5388497 2.682641            0  369
 2:             5990979_Cell_Cycle,_Mitotic 0.0001252976 0.002356366 0.5594755 2.738270            0  317
 3:        5991210_Signaling_by_Rho_GTPases 0.0001310444 0.002356366 0.4238512 2.009963            0  231
 4:                         5991454_M_Phase 0.0001372872 0.002356366 0.5576247 2.548794            0  173
 5:     5991023_Metabolism_of_carbohydrates 0.0001378930 0.002356366 0.4944766 2.240678            0  160
 6:            5991209_RHO_GTPase_Effectors 0.0001386001 0.002356366 0.5248796 2.369573            0  157
 7:  5991502_Mitotic_Metaphase_and_Anaphase 0.0001450326 0.002356366 0.6052907 2.633267            0  123
 8:                5991600_Mitotic_Anaphase 0.0001452222 0.002356366 0.6015521 2.613626            0  122
 9: 5991599_Separation_of_Sister_Chromatids 0.0001460707 0.002356366 0.6164600 2.661433            0  116
10:           5991130_Programmed_Cell_Death 0.0001465201 0.002356366 0.4537506 1.941445            0  108

One can make an enrichment plot for a pathway:

plotEnrichment(examplePathways[["5991130_Programmed_Cell_Death"]],
               exampleRanks) + labs(title="Programmed Cell Death")

Or make a table plot for a bunch of selected pathways:

topPathwaysUp <- fgseaRes[ES > 0, ][head(order(pval), n=10), pathway]
topPathwaysDown <- fgseaRes[ES < 0, ][head(order(pval), n=10), pathway]
topPathways <- c(topPathwaysUp, rev(topPathwaysDown))
plotGseaTable(examplePathways[topPathways], exampleRanks, fgseaRes, 
              gseaParam = 0.5)