Using fgsea package

Alexey Sergushichev

2016-06-22

fgsea is an R-package for fast preranked gene set enrichment analysis (GSEA). The performance is achieved by using an algorithm for cumulative GSEA-statistic calculation. This allows to reuse samples between different gene set sizes. See the preprint for algorithmic details.

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 0.0001226242 0.002059544 0.5388497
## 2:         5990979_Cell_Cycle,_Mitotic 0.0001248128 0.002059544 0.5594755
## 3:    5991210_Signaling_by_Rho_GTPases 0.0001310273 0.002059544 0.4238512
## 4:                     5991454_M_Phase 0.0001367989 0.002059544 0.5576247
## 5: 5991023_Metabolism_of_carbohydrates 0.0001378930 0.002059544 0.4944766
## 6:        5991209_RHO_GTPase_Effectors 0.0001381215 0.002059544 0.5248796
##         NES nMoreExtreme size                             leadingEdge
## 1: 2.675218            0  369   66336,66977,12442,107995,66442,19361,
## 2: 2.741942            0  317   66336,66977,12442,107995,66442,12571,
## 3: 2.008311            0  231 66336,66977,20430,104215,233406,107995,
## 4: 2.543197            0  173   66336,66977,12442,107995,66442,52276,
## 5: 2.236375            0  160    11676,21991,15366,58250,12505,20527,
## 6: 2.366843            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] 74

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
## 1:                                                    Meiotic Synapsis
## 2:                                      Rora activates gene expression
## 3:               Bmal1:Clock,Npas2 activates circadian gene expression
## 4:                       Translocation of Glut4 to the Plasma Membrane
## 5:                        Endocrine-committed (Ngn3+) progenitor cells
## 6: Late stage (branching morphogenesis) pancreatic bud precursor cells
##         pval      padj         ES        NES nMoreExtreme size
## 1: 0.5278246 0.7829761  0.2885754  0.9433875          312   27
## 2: 0.8458574 0.9307303 -0.3087414 -0.6587362          438    5
## 3: 0.4411765 0.7377005  0.4209054  1.0216385          224    9
## 4: 0.6810207 0.8735949  0.2387284  0.8462790          426   39
## 5: 0.4423077 0.7377005  0.6477746  1.0424929          206    2
## 6: 0.9494382 0.9756201 -0.3460577 -0.5553623          506    2
##                             leadingEdge
## 1:              15270,12189,71846,19357
## 2:             20787,328572,12753,11865
## 3:                    20893,59027,19883
## 4: 17918,19341,20336,22628,22627,20619,
## 5:                          18088,18506
## 6:                          15205,11925

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: 0.5445205 0.7234832  0.2885754  0.9483339          317   27
## 2: 0.6800643 0.8250884  0.2387284  0.8479355          422   39
## 3: 0.1138614 0.2623676 -0.3640706 -1.3315425           45   31
## 4: 0.7952468 0.8979087  0.2516324  0.7320343          434   17
## 5: 0.3512706 0.5603723  0.2469065  1.0653505          234  106
## 6: 0.4351005 0.6326772  0.3607407  1.0494457          237   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