A common approach in analyzing gene expression profiles was identifying differential expressed genes that are deemed interesting. The enrichment analysis we demonstrated in Disease enrichment analysis vignette were based on these differential expressed genes. This approach will find genes where the difference is large, but it will not detect a situation where the difference is small, but evidenced in coordinated way in a set of related genes. Gene Set Enrichment Analysis (GSEA)1 directly addresses this limitation. All genes can be used in GSEA; GSEA aggregates the per gene statistics across genes within a gene set, therefore making it possible to detect situations where all genes in a predefined set change in a small but coordinated way. Since it is likely that many relevant phenotypic differences are manifested by small but consistent changes in a set of genes.
Genes are ranked based on their phenotypes. Given a priori defined set of gens S (e.g., genes shareing the same DO category), the goal of GSEA is to determine whether the members of S are randomly distributed throughout the ranked gene list (L) or primarily found at the top or bottom.
There are three key elements of the GSEA method:
We implemented GSEA algorithm proposed by Subramanian1. Alexey Sergushichev implemented an algorithm for fast GSEA analysis in the fgsea2 package.
In DOSE3, user can use GSEA algorithm implemented in DOSE or fgsea by specifying the parameter by="DOSE" or by="fgsea". By default, DOSE use fgsea since it is much more fast.
Leading edge analysis reports Tags to indicate the percentage of genes contributing to the enrichment score, List to indicate where in the list the enrichment score is attained and Signal for enrichment signal strength.
It would also be very interesting to get the core enriched genes that contribute to the enrichment.
DOSE supports leading edge analysis and report core enriched genes in GSEA analysis.
gseDO fuctionIn the following example, in order to speedup the compilation of this document, only gene sets with size above 120 were tested and only 100 permutations were performed.
library(DOSE)
data(geneList)
y <- gseDO(geneList,
nPerm = 100,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
head(y, 3)## ID Description setSize
## DOID:0060037 DOID:0060037 developmental disorder of mental health 315
## DOID:374 DOID:374 nutrition disease 313
## DOID:9970 DOID:9970 obesity 289
## enrichmentScore NES pvalue p.adjust qvalues rank
## DOID:0060037 -0.3407349 -1.484584 0.01149425 0.1589069 0.1310462 2313
## DOID:374 -0.3421127 -1.490076 0.01149425 0.1589069 0.1310462 1464
## DOID:9970 -0.3316172 -1.441877 0.01149425 0.1589069 0.1310462 1447
## leading_edge
## DOID:0060037 tags=24%, list=19%, signal=20%
## DOID:374 tags=22%, list=12%, signal=20%
## DOID:9970 tags=20%, list=12%, signal=18%
## core_enrichment
## DOID:0060037 154/1760/9732/7337/5175/6532/4763/54806/9759/6326/1499/7157/221037/627/2908/3399/2571/3082/23503/3791/51265/27347/55650/596/3067/51185/7552/22829/23426/324/5021/4885/7248/8910/8604/3397/4208/3400/26470/553/3953/6812/64221/80208/5172/9037/3952/477/93664/3625/2944/6925/6594/7102/3908/2550/4915/4922/26960/1746/2697/6863/3913/2891/367/4128/150/7166/6505/5348/18/4129/9370/57502/4137/79083
## DOID:374 2169/1490/7840/4887/4314/595/4018/6403/590/3087/866/66036/5919/5176/3953/164656/5950/2638/2166/5243/5468/5108/10560/4023/3485/7350/3952/1149/585/1513/3489/79068/4671/477/4313/3625/9369/6720/7494/2099/3480/3991/23446/6678/4915/5167/8228/165/2152/185/367/4982/3667/4128/9607/3572/150/563/1489/3479/9370/9122/5105/2167/5346/79689/5241
## DOID:9970 1490/7840/4887/4314/595/4018/6403/590/3087/866/66036/5919/5176/3953/5950/2638/2166/5243/5468/4023/3485/7350/3952/1149/585/1513/3489/79068/4671/4313/3625/9369/6720/7494/2099/3480/3991/6678/4915/5167/8228/165/367/4982/3667/4128/9607/3572/150/563/3479/9370/9122/5105/2167/5346/79689/5241gseNCG fuctionncg <- gseNCG(geneList,
nPerm = 100,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
ncg <- setReadable(ncg, 'org.Hs.eg.db')
head(ncg, 3)## ID Description setSize enrichmentScore NES pvalue
## breast breast breast 133 -0.4869070 -1.950820 0.01298701
## lung lung lung 173 -0.3880662 -1.582730 0.01408451
## lymphoma lymphoma lymphoma 188 0.2999589 1.360702 0.03703704
## p.adjust qvalues rank leading_edge
## breast 0.04225352 0.02223870 2930 tags=33%, list=23%, signal=26%
## lung 0.04225352 0.02223870 2775 tags=31%, list=22%, signal=25%
## lymphoma 0.07407407 0.03898635 2087 tags=21%, list=17%, signal=18%
## core_enrichment
## breast KMT2A/ERBB3/SETD2/ARID1A/GPS2/NCOR1/RB1/MAP2K4/NF1/TP53/PIK3R1/STK11/CDKN1B/PTGFR/APC/CCND1/TRAF5/MAP3K1/ESR1/TBX3/FOXA1/GATA3
## lung SETD2/ATXN3L/LRP1B/BRD3/ARID1A/INHBA/RB1/ADCY1/LYRM9/NF1/CTNNB1/TP53/SATB2/STK11/CTIF/CTNNA3/KDR/COL11A1/FLT3/APC/ADGRL3/FGFR3/NCAM2/DIP2C/APLNR/SLIT2/EPHA3/RUNX1T1/ZMYND10/ZFHX4/GLI3/TNN/PLSCR4/DACH1/ERBB4
## lymphoma DUSP2/EZH2/PRDM1/MYC/ZWILCH/IKZF3/PLCG2/IDH2/HIST1H1C/MAGEC3/CD79B/ETV6/HIST1H1E/HIST1H1B/IRF8/CD28/SLC29A2/DUSP9/TNFAIP3/DNMT3A/SYK/TNF/BCR/HIST1H1D/DSC3/UBE2A/PABPC1gseDGN fuctiondgn <- gseDGN(geneList,
nPerm = 100,
minGSSize = 120,
pvalueCutoff = 0.2,
pAdjustMethod = "BH",
verbose = FALSE)
dgn <- setReadable(dgn, 'org.Hs.eg.db')
head(dgn, 3)## ID Description setSize
## umls:C0032914 umls:C0032914 Pre-Eclampsia 334
## umls:C0338656 umls:C0338656 Impaired cognition 342
## umls:C1263846 umls:C1263846 Attention deficit hyperactivity disorder 321
## enrichmentScore NES pvalue p.adjust qvalues rank
## umls:C0032914 -0.3066475 -1.325055 0.01219512 0.1488095 0.1065163 1909
## umls:C0338656 -0.3266625 -1.405975 0.01219512 0.1488095 0.1065163 1997
## umls:C1263846 -0.3132898 -1.349038 0.01219512 0.1488095 0.1065163 2176
## leading_edge
## umls:C0032914 tags=29%, list=15%, signal=25%
## umls:C0338656 tags=23%, list=16%, signal=20%
## umls:C1263846 tags=25%, list=17%, signal=21%
## core_enrichment
## umls:C0032914 PLAC1/PSG5/ERCC2/ADD1/ACTG2/PECAM1/PGF/VEGFC/DDAH2/F7/PDE5A/ADAM12/CAPN10/LTF/SOD3/COL4A6/TEK/IL5/PRCP/HPGD/SCNN1A/MBL2/CYP1A1/IL1R1/INSR/PROC/HP/VWF/HDC/EFNA1/FABP2/MMP3/NPR1/OXTR/LPA/EDIL3/MGP/APLNR/PYGM/SELP/FGF1/GJA4/FGF14/MMP13/SLC22A5/COL1A2/ANG/COL1A1/LEPR/PROS1/FGF2/PPARG/CRHBP/SYNPO/COL3A1/LPL/THBD/MMP10/COL5A2/LEP/PTGER3/MMP2/PDGFC/GSTM1/CFH/NOV/ESR1/IGF1R/TPBG/HSPA1L/HSPG2/VCAN/COL5A1/SPARC/NR3C2/CLU/ENPP1/F13A1/HTRA1/F3/AGTR1/GSTT1/PLAT/AR/IRS1/IL6ST/COL4A5/THBS4/IGF1/ELN/ADIPOQ/CORIN/HLA-DQA1/FABP4/CX3CR1
## umls:C0338656 NR3C1/CAPN3/SLC2A10/CREBBP/ZNF224/ITM2B/ELK3/CLN5/GAD1/BACE1/HGF/SERPINA3/MBL2/SST/EGR1/INSR/UTRN/ARL4D/PVALB/EEF1A2/DYM/CD36/RAB40AL/RBMS3/TREM2/PER3/OXTR/TSC1/CDR1/IGFALS/TPPP/SELP/NGF/BCHE/KCNS3/APBB2/TRPM4/RUNX1T1/MME/ABCB1/PPARG/MVP/NME8/SPG11/LPL/SLC26A4/FHL5/KL/LEP/FTO/NAIP/SORL1/ESR1/ABCC8/CST3/LAMA2/HHAT/LRP1/CLU/ALB/SPON1/NTS/HTRA1/GSTT1/GRIA2/MAGI2/IRS1/TAT/COL4A5/AASS/IGF1/ITPR1/BMP4/LRP2/MAPT/ERBB4/GRP
## umls:C1263846 MYT1/EPB41L3/MAN2A2/DAB2/MAP1B/EMP2/BACH2/BDNF/CHPT1/NR3C1/IGBP1/DEAF1/CREBBP/ELK3/HKDC1/RARA/GRM5/TSPAN31/HTR1A/CEP112/VWA8/CYP2A6/FHIT/PRKG1/F2R/COL2A1/NGFR/PDLIM1/DBH/LGALS8/OXTR/FLNC/ADGRL3/SCARA3/VAMP2/NR4A2/MEIS2/PTPRN2/NELL2/NGF/UNC5B/BCHE/TGFB2/LIN7A/NRP1/AVPR1B/RPS2P45/ARSD/REEP5/DACT1/AGBL3/PPARG/TSPAN1/CDH13/SHH/ACKR3/LEP/FTO/PER2/ATXN1/CDH11/NTF3/TCIM/ASPA/EDNRA/NR3C2/ZNF385D/NTRK2/PCSK5/BMPR1B/PTHLH/SYT1/ASTN2/GJA1/LAMB2/MAOA/ADRA2A/TPH1/MAOB/ZNF4231. Subramanian, A. et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102, 15545–15550 (2005).
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