Gene set enrichment analyses, runs 'fgsea' package implementation with preset values.

GSEA(genes, geneset)

Arguments

genes

DA gene names to be checked if they are over-represented or not.

geneset

Pathways to be used in enrichment analyses. If not set vp2008 (Chaussabel, 2008) immune modules will be used. This can be set to any geneset using `read.gmt` function from `qusage` package. Different modules are available: https://www.gsea-msigdb.org/gsea/downloads.jsp.

Value

data.frame, list of pathways and their enrichment (adjusted) p-values.

References

G. Korotkevich, V. Sukhov, A. Sergushichev. Fast gene set enrichment analysis. bioRxiv (2019), doi:10.1101/060012

Examples

# \donttest{
library(cinaR)
library(fgsea)
data(examplePathways)
data(exampleRanks)
GSEA(exampleRanks, examplePathways)
#>                                                                                      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
#>  ---                                                                                        
#> 582:                                                          6096057_Adaptive_Immune_System
#> 583:                          6096937_Class_I_MHC_mediated_antigen_processing_&_presentation
#> 584:                                                            6096957_ER-Phagosome_pathway
#> 585:                                           6096958_Antigen_processing-Cross_presentation
#> 586:                                                            912497_Meiotic_Recombination
#>            pval       padj    log2err         ES        NES size
#>   1: 0.56790123 0.73622813 0.06628422  0.2885754  0.9314811   27
#>   2: 0.67500000 0.82406250 0.05536428  0.2387284  0.8437176   39
#>   3: 0.10765550 0.26254480 0.21140019 -0.3640706 -1.3489029   31
#>   4: 0.80219780 0.88362389 0.05174055  0.2516324  0.7309702   17
#>   5: 0.36538462 0.56051148 0.07977059  0.2469065  1.0516851  106
#>  ---                                                            
#> 582: 0.70905923 0.83697073 0.05513518  0.2568888  0.8203128   25
#> 583: 0.70905923 0.83697073 0.05513518  0.2568888  0.8203128   25
#> 584: 0.34385965 0.54305045 0.09255289  0.3477495  1.0987062   23
#> 585: 0.70905923 0.83697073 0.05513518  0.2568888  0.8203128   25
#> 586: 0.01173541 0.05372619 0.38073040  0.5722386  1.6947710   18
#>                                                                                     leadingEdge
#>   1:                                                                    15270,12189,71846,19357
#>   2: 17918,19341,20336,22628,22627,20619,16579,16568,11651,12315,12314,11465,53413,107371,12313
#>   3:                  76199,19014,26896,229003,17977,17978,12537,70208,67381,59024,327987,20602
#>   4:                                                                          20893,59027,19883
#>   5:             60406,19361,15270,20893,12189,68240,71846,20018,192191,12567,19891,59027,19357
#>  ---                                                                                           
#> 582:       26440,26444,26445,19170,26442,19177,667803,12317,53421,19172,19166,19173,16913,26441
#> 583:       26440,26444,26445,19170,26442,19177,667803,12317,53421,19172,19166,19173,16913,26441
#> 584:       26440,26444,26445,19170,26442,19177,667803,12317,53421,19172,19166,19173,16913,26441
#> 585:       26440,26444,26445,19170,26442,19177,667803,12317,53421,19172,19166,19173,16913,26441
#> 586:                                                        19361,15270,12189,68240,12567,19891
# }