Using fgsea package

fgsea is an R-package for fast preranked gene set enrichment analysis (GSEA). This package allows to quickly and accurately calculate arbitrarily low GSEA P-values for a collection of gene sets. P-value estimation is based on an adaptive multi-level split Monte-Carlo scheme. See the preprint for algorithmic details.

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 2.425267e-26 1.421206e-23
## 2:                     5990979_Cell_Cycle,_Mitotic 9.083795e-26 2.661552e-23
## 3:                    5991851_Mitotic_Prometaphase 3.606401e-19 7.044503e-17
## 4: 5992217_Resolution_of_Sister_Chromatid_Cohesion 2.012757e-17 2.948689e-15
## 5:                                 5991454_M_Phase 2.302068e-14 2.698024e-12
## 6:          5991502_Mitotic_Metaphase_and_Anaphase 3.261432e-14 3.185332e-12
##      log2err        ES      NES size                              leadingEdge
## 1: 1.3344975 0.5388497 2.698295  369 66336,66977,12442,107995,66442,19361,...
## 2: 1.3188888 0.5594755 2.748676  317 66336,66977,12442,107995,66442,12571,...
## 3: 1.1330899 0.7253270 2.938624   82 66336,66977,12442,107995,66442,52276,...
## 4: 1.0768682 0.7347987 2.928349   74 66336,66977,12442,107995,66442,52276,...
## 5: 0.9759947 0.5576247 2.548831  173 66336,66977,12442,107995,66442,52276,...
## 6: 0.9653278 0.6052907 2.635453  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 0.828685259
## 2: A tetrasaccharide linker sequence is required for GAG synthesis 0.703564728
## 3:                           ABC transporters in lipid homeostasis 0.190170940
## 4:                          ABC-family proteins mediated transport 0.321804511
## 5:                                    ABO blood group biosynthesis 0.984000000
## 6:                       ADP signalling through P2Y purinoceptor 1 0.007280375
##          padj    log2err         ES        NES size
## 1: 0.93668770 0.05412006 -0.5732978 -0.7651662    1
## 2: 0.87744955 0.05896945  0.3218180  0.8238387   11
## 3: 0.54612055 0.14641624 -0.4385385 -1.2584304   12
## 4: 0.68900670 0.08756971  0.2771011  1.0868395   67
## 5: 0.99308308 0.04641550  0.5120427  0.6799784    1
## 6: 0.07172067 0.40701792  0.6228710  1.7942082   16
##                                 leadingEdge
## 1:                                    19139
## 2: 14733,20971,20970,12032,29873,218271,...
## 3: 19299,27403,11307,11806,217265,27409,...
## 4: 56199,17463,26440,26444,19179,228769,...
## 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 runnig 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: 0.4956217 0.6674260 0.07271411  0.2885754  0.9457051   27
## 2: 0.6644628 0.8065505 0.05571042  0.2387284  0.8597837   39
## 3: 0.1274038 0.2998340 0.19381330 -0.3640706 -1.3129641   31
## 4: 0.7742537 0.8753333 0.05423159  0.2516324  0.7431917   17
## 5: 0.3552632 0.5551579 0.08063885  0.2469065  1.0536221  106
## 6: 0.3880597 0.5860902 0.08916471  0.3607407  1.0654411   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