Using fgsea package

Loading necessary libraries

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

Quick run

Loading example pathways and gene-level statistics and setting random seed:

data(examplePathways)
data(exampleRanks)
set.seed(42)

Running fgsea:

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

The resulting table contains enrichment scores and p-values:

head(fgseaRes[order(pval), ])

##                                            pathway         pval         padj
## 1:                     5990979_Cell_Cycle,_Mitotic 6.690481e-27 3.920622e-24
## 2:                              5990980_Cell_Cycle 3.312565e-26 9.705816e-24
## 3:                    5991851_Mitotic_Prometaphase 8.470173e-19 1.654507e-16
## 4: 5992217_Resolution_of_Sister_Chromatid_Cohesion 2.176649e-18 3.188791e-16
## 5:                                 5991454_M_Phase 1.873997e-14 2.196325e-12
## 6:         5991599_Separation_of_Sister_Chromatids 8.733223e-14 8.529448e-12
##      log2err        ES      NES size                              leadingEdge
## 1: 1.3422338 0.5594755 2.769070  317 66336,66977,12442,107995,66442,12571,...
## 2: 1.3267161 0.5388497 2.705894  369 66336,66977,12442,107995,66442,19361,...
## 3: 1.1239150 0.7253270 2.972690   82 66336,66977,12442,107995,66442,52276,...
## 4: 1.1053366 0.7347987 2.957518   74 66336,66977,12442,107995,66442,52276,...
## 5: 0.9759947 0.5576247 2.554076  173 66336,66977,12442,107995,66442,52276,...
## 6: 0.9545416 0.6164600 2.670030  116 66336,66977,107995,66442,52276,67629,...

As you can see from the warning, fgsea has a default lower bound eps=1e-10 for estimating P-values. If you need to estimate P-value more accurately, you can set the eps argument to zero in the fgsea function.

fgseaRes <- fgsea(pathways = examplePathways, 
                  stats    = exampleRanks,
                  eps      = 0.0,
                  minSize  = 15,
                  maxSize  = 500)

head(fgseaRes[order(pval), ])

##                                            pathway         pval         padj
## 1:                              5990980_Cell_Cycle 2.535645e-26 1.485888e-23
## 2:                     5990979_Cell_Cycle,_Mitotic 9.351994e-26 2.740134e-23
## 3:                    5991851_Mitotic_Prometaphase 3.633805e-19 7.098033e-17
## 4: 5992217_Resolution_of_Sister_Chromatid_Cohesion 2.077985e-17 3.044248e-15
## 5:                                 5991454_M_Phase 2.251818e-14 2.639131e-12
## 6:          5991502_Mitotic_Metaphase_and_Anaphase 3.196758e-14 3.122167e-12
##      log2err        ES      NES size                              leadingEdge
## 1: 1.3344975 0.5388497 2.664606  369 66336,66977,12442,107995,66442,19361,...
## 2: 1.3188888 0.5594755 2.740246  317 66336,66977,12442,107995,66442,12571,...
## 3: 1.1330899 0.7253270 2.926512   82 66336,66977,12442,107995,66442,52276,...
## 4: 1.0768682 0.7347987 2.920436   74 66336,66977,12442,107995,66442,52276,...
## 5: 0.9759947 0.5576247 2.547515  173 66336,66977,12442,107995,66442,52276,...
## 6: 0.9653278 0.6052907 2.639370  123 66336,66977,107995,66442,52276,67629,...

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 in a text format data:table::fwrite function can be used:

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

To make leading edge more human-readable it can be converted using mapIdsList (similar to AnnotationDbi::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 := mapIdsList(
                                     x=org.Mm.eg.db, 
                                     keys=leadingEdge,
                                     keytype="ENTREZID", 
                                     column="SYMBOL")]
fwrite(fgseaResMain, file="fgseaResMain.txt", sep="\t", sep2=c("", " ", ""))

Performance considerations

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 bplapply 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, maxSize=500)
head(fgseaRes)

##                                                            pathway        pval
## 1:                      5-Phosphoribose 1-diphosphate biosynthesis 0.879921260
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.509842520
## 3:                           ABC transporters in lipid homeostasis 0.244258873
## 4:                          ABC-family proteins mediated transport 0.400000000
## 5:                                    ABO blood group biosynthesis 0.975708502
## 6:                       ADP signalling through P2Y purinoceptor 1 0.006987104
##          padj    log2err         ES        NES size
## 1: 0.95233803 0.05080541 -0.5732978 -0.7547789    1
## 2: 0.77031899 0.07727470  0.3755168  0.9506349   10
## 3: 0.58861839 0.12563992 -0.4385385 -1.2221901   12
## 4: 0.70871773 0.08085892  0.2614189  1.0373162   66
## 5: 0.98830682 0.04735342  0.5120427  0.6814020    1
## 6: 0.06731181 0.40701792  0.6228710  1.7896731   16
##                                 leadingEdge
## 1:                                    19139
## 2: 14733,20971,20970,12032,29873,218271,...
## 3: 19299,27403,11307,11806,217265,27409,...
## 4: 17463,26440,26444,19179,228769,56325,...
## 5:                                    14344
## 6:  14696,14702,14700,14682,14676,66066,...

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 running fgsea:

fgseaRes <- fgsea(pathways, ranks, minSize=15, maxSize=500)
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    log2err         ES        NES size
## 1: 0.5483333 0.7251271 0.06523531  0.2885754  0.9412385   27
## 2: 0.6857610 0.8336764 0.05378728  0.2387284  0.8387184   39
## 3: 0.1362468 0.3150882 0.19381330 -0.3640706 -1.3431389   31
## 4: 0.7779661 0.8806609 0.04959020  0.2516324  0.7382131   17
## 5: 0.3884892 0.5913108 0.07511816  0.2469065  1.0473836  106
## 6: 0.4101695 0.6090340 0.08085892  0.3607407  1.0583037   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