Using fgsea package

Loading necessary libraryries

Quick run

Loading example pathways and gene-level statistics:

data(examplePathways)
data(exampleRanks)

Running fgsea:

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

The resulting table contains enrichment scores and p-values:

head(fgseaRes[order(pval), ])

##                                pathway         pval        padj        ES
## 1:                  5990980_Cell_Cycle 0.0001237317 0.002083067 0.5388497
## 2:         5990979_Cell_Cycle,_Mitotic 0.0001270003 0.002083067 0.5594755
## 3:    5991210_Signaling_by_Rho_GTPases 0.0001325205 0.002083067 0.4238512
## 4:                     5991454_M_Phase 0.0001372307 0.002083067 0.5576247
## 5: 5991023_Metabolism_of_carbohydrates 0.0001385809 0.002083067 0.4944766
## 6:        5991209_RHO_GTPase_Effectors 0.0001388118 0.002083067 0.5248796
##         NES nMoreExtreme size                             leadingEdge
## 1: 2.686396            0  369   66336,66977,12442,107995,66442,19361,
## 2: 2.748593            0  317   66336,66977,12442,107995,66442,12571,
## 3: 2.015763            0  231 66336,66977,20430,104215,233406,107995,
## 4: 2.554097            0  173   66336,66977,12442,107995,66442,52276,
## 5: 2.245572            0  160    11676,21991,15366,58250,12505,20527,
## 6: 2.379476            0  157 66336,66977,20430,104215,233406,107995,

It takes about ten seconds to get results with significant hits after FDR correction:

sum(fgseaRes[, padj < 0.01])

## [1] 77

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)

Performance considerations

Please, be aware that fgsea function takes about O(nk^{3/2}) time, where n is number of permutations and k is a maximal size of the pathways. That means that setting maxSize parameter with a value of ~500 is strongly recommended.

Also, fgsea is parallelized using BiocParallel package. By default the first registered backend returned by bpparam() is used. To tweak the parallelization one can either specify BPPARAM parameter used for bclapply of set nproc parameter, which is a shorthand for setting BPPARAM=MulticoreParam(workers = nproc).

Using Reactome pathways

For convenience there is reactomePathways function that obtains pathways from Reactome for given set of genes. Package reactome.db is required to be installed.

pathways <- reactomePathways(names(exampleRanks))
fgseaRes <- fgsea(pathways, exampleRanks, nperm=1000, maxSize=500)
head(fgseaRes)

##                                                      pathway        pval
## 1:                                                 Apoptosis 0.001533742
## 2:                                                Hemostasis 0.011842105
## 3:                           Intrinsic Pathway for Apoptosis 0.001748252
## 4: Cleavage of Growing Transcript in the Termination Region  0.348284960
## 5:                                       PKB-mediated events 0.745596869
## 6:                                              PI3K Cascade 0.888382688
##          padj         ES        NES nMoreExtreme size
## 1: 0.02691308  0.5163094  2.0818792            0   67
## 2: 0.09701661  0.2901862  1.4079592            8  298
## 3: 0.02691308  0.6758615  2.2513379            0   29
## 4: 0.66447392 -0.2577034 -1.0604061          131   50
## 5: 0.89497310  0.6268022  0.8397351          380    1
## 6: 0.96320048 -0.2148882 -0.6760628          389   18
##                               leadingEdge
## 1:   58801,14958,97165,22352,12043,14103,
## 2:   12306,12505,71946,16184,14062,16185,
## 3:   58801,12043,12367,14940,14942,12018,
## 4: 67332,331401,225027,68011,74737,57317,
## 5:                                  18576
## 6:  19247,18607,75669,327826,14180,14182,

Starting from files

One can also start from .rnk and .gmt files as in original GSEA:

rnk.file <- system.file("extdata", "naive.vs.th1.rnk", package="fgsea")
gmt.file <- system.file("extdata", "mouse.reactome.gmt", package="fgsea")

Loading ranks:

ranks <- read.table(rnk.file,
                    header=TRUE, colClasses = c("character", "numeric"))
ranks <- setNames(ranks$t, ranks$ID)
str(ranks)

##  Named num [1:12000] -63.3 -49.7 -43.6 -41.5 -33.3 ...
##  - attr(*, "names")= chr [1:12000] "170942" "109711" "18124" "12775" ...

Loading pathways:

pathways <- gmtPathways(gmt.file)
str(head(pathways))

## List of 6
##  $ 1221633_Meiotic_Synapsis                                                : chr [1:64] "12189" "13006" "15077" "15078" ...
##  $ 1368092_Rora_activates_gene_expression                                  : chr [1:9] "11865" "12753" "12894" "18143" ...
##  $ 1368110_Bmal1:Clock,Npas2_activates_circadian_gene_expression           : chr [1:16] "11865" "11998" "12753" "12952" ...
##  $ 1445146_Translocation_of_Glut4_to_the_Plasma_Membrane                   : chr [1:55] "11461" "11465" "11651" "11652" ...
##  $ 186574_Endocrine-committed_Ngn3+_progenitor_cells                       : chr [1:4] "18012" "18088" "18506" "53626"
##  $ 186589_Late_stage_branching_morphogenesis_pancreatic_bud_precursor_cells: chr [1:4] "11925" "15205" "21410" "246086"

And runnig fgsea:

fgseaRes <- fgsea(pathways, ranks, minSize=15, maxSize=500, nperm=1000)
head(fgseaRes)

##                                                                                    pathway
## 1:                                                                1221633_Meiotic_Synapsis
## 2:                                   1445146_Translocation_of_Glut4_to_the_Plasma_Membrane
## 3: 442533_Transcriptional_Regulation_of_Adipocyte_Differentiation_in_3T3-L1_Pre-adipocytes
## 4:                                                                  508751_Circadian_Clock
## 5:                                               5334727_Mus_musculus_biological_processes
## 6:                                        573389_NoRC_negatively_regulates_rRNA_expression
##         pval      padj         ES        NES nMoreExtreme size
## 1: 0.5482234 0.7203114  0.2885754  0.9422634          323   27
## 2: 0.6827697 0.8277870  0.2387284  0.8542766          423   39
## 3: 0.1132530 0.2674334 -0.3640706 -1.3572976           46   31
## 4: 0.7843478 0.8825758  0.2516324  0.7411287          450   17
## 5: 0.3578501 0.5518425  0.2469065  1.0596758          252  106
## 6: 0.3843478 0.5821058  0.3607407  1.0624836          220   17
##                              leadingEdge
## 1:               15270,12189,71846,19357
## 2:  17918,19341,20336,22628,22627,20619,
## 3: 20602,327987,59024,67381,70208,12537,
## 4:                     20893,59027,19883
## 5:  60406,19361,15270,20893,12189,68240,
## 6:              60406,20018,245688,20017