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 0.0001236552 0.002249745 0.5388497
## 2:         5990979_Cell_Cycle,_Mitotic 0.0001263424 0.002249745 0.5594755
## 3:    5991210_Signaling_by_Rho_GTPases 0.0001327140 0.002249745 0.4238512
## 4:                     5991454_M_Phase 0.0001386578 0.002249745 0.5576247
## 5: 5991023_Metabolism_of_carbohydrates 0.0001393146 0.002249745 0.4944766
## 6:        5991209_RHO_GTPase_Effectors 0.0001397038 0.002249745 0.5248796
##         NES nMoreExtreme size                             leadingEdge
## 1: 2.681731            0  369   66336,66977,12442,107995,66442,19361,
## 2: 2.748382            0  317   66336,66977,12442,107995,66442,12571,
## 3: 2.012955            0  231 66336,66977,20430,104215,233406,107995,
## 4: 2.556826            0  173   66336,66977,12442,107995,66442,52276,
## 5: 2.246723            0  160    11676,21991,15366,58250,12505,20527,
## 6: 2.378129            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] 72

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)

From the plot above one can see that there are very similar pathways in the table (for example 5991502_Mitotic_Metaphase_and_Anaphase and 5991600_Mitotic_Anaphase). To select only independent pathways one can use collapsePathways function:

collapsedPathways <- collapsePathways(fgseaRes[order(pval)][padj < 0.01], 
                                      examplePathways, exampleRanks)
mainPathways <- fgseaRes[pathway %in% collapsedPathways$mainPathways][
                         order(-NES), pathway]
plotGseaTable(examplePathways[mainPathways], exampleRanks, fgseaRes, 
              gseaParam = 0.5)

To save the results data:table::fwrite function can be used:

library(data.table)
fwrite(fgseaRes, file="fgseaRes.txt", sep="\t", sep2=c("", " ", ""))

To make leading edge more human-readable it can be converted using mapIds function and a corresponding database (here org.Mm.eg.db for mouse):

library(org.Mm.eg.db)
fgseaResMain <- fgseaRes[match(mainPathways, pathway)]
fgseaResMain[, leadingEdge := lapply(leadingEdge, mapIds, x=org.Mm.eg.db, keytype="ENTREZID", column="SYMBOL")]
fwrite(fgseaResMain, file="fgseaResMain.txt", sep="\t", sep2=c("", " ", ""))

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
## 1:                      5-Phosphoribose 1-diphosphate biosynthesis
## 2: A tetrasaccharide linker sequence is required for GAG synthesis
## 3:                           ABC transporters in lipid homeostasis
## 4:                          ABC-family proteins mediated transport
## 5:                       ADP signalling through P2Y purinoceptor 1
## 6:                      ADP signalling through P2Y purinoceptor 12
##          pval      padj         ES        NES nMoreExtreme size
## 1: 0.90120968 0.9616334  0.4267378  0.6736673          446    2
## 2: 0.63431542 0.8452090  0.3218180  0.8479526          365   11
## 3: 0.26150121 0.5964535 -0.3850118 -1.1517149          107   13
## 4: 0.32415902 0.6507637  0.2709658  1.0690737          211   66
## 5: 0.01379310 0.1156384  0.6228710  1.7879710            7   16
## 6: 0.04074703 0.2072087  0.5959844  1.6277373           23   13
##                              leadingEdge
## 1:                          328099,19139
## 2: 14733,20971,20970,12032,29873,218271,
## 3: 19299,27403,11307,11806,217265,27409,
## 4: 18669,56199,17463,26444,19179,228769,
## 5:  14696,14702,14700,14682,14676,66066,
## 6:  14696,14702,14700,66066,14697,14693,

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: 0.5294118 0.7099206  0.2885754  0.9381393          314   27
## 2: 0.6944444 0.8441260  0.2387284  0.8412340          424   39
## 3: 0.1305419 0.3076115 -0.3640706 -1.3241110           52   31
## 4: 0.8184991 0.9039418  0.2516324  0.7282192          468   17
## 5: 0.3801170 0.5898179  0.2469065  1.0407714          259  106
## 6: 0.4240838 0.6347742  0.3607407  1.0439764          242   17
##                              leadingEdge
## 1:               15270,12189,71846,19357
## 2:  17918,19341,20336,22628,22627,20619,
## 3: 76199,19014,26896,229003,17977,17978,
## 4:                     20893,59027,19883
## 5:  60406,19361,15270,20893,12189,68240,
## 6:              60406,20018,245688,20017