Contents

1 GSEA algorithm

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.

1.1 Leading edge analysis and core enriched genes

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.

1.2 gseDO fuction

In 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:1492       DOID:1492                  eye and adnexa disease     459
## DOID:5614       DOID:5614                             eye disease     450
## DOID:0060037 DOID:0060037 developmental disorder of mental health     315
##              enrichmentScore       NES     pvalue   p.adjust    qvalues rank
## DOID:1492         -0.3105160 -1.381007 0.01162791 0.08504875 0.04504761 1793
## DOID:5614         -0.3125247 -1.388125 0.01176471 0.08504875 0.04504761 1768
## DOID:0060037      -0.3407349 -1.474516 0.01315789 0.08504875 0.04504761 2313
##                                leading_edge
## DOID:1492    tags=22%, list=14%, signal=19%
## DOID:5614    tags=22%, list=14%, signal=19%
## DOID:0060037 tags=24%, list=19%, signal=20%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         core_enrichment
## DOID:1492    3371/3082/5914/2878/4153/3791/23247/1543/80184/6750/1958/2098/7450/596/9187/2034/482/948/1490/1280/3931/5737/4314/4881/2261/3426/187/629/6403/7042/6785/7507/2934/5176/4060/1277/7078/5950/2057/727/10516/4311/2247/1295/358/10203/2192/582/10218/57125/3485/585/1675/6310/2202/4313/2944/4254/3075/1501/2099/3480/4653/1195/6387/3305/1471/857/4016/1909/4053/6678/1296/7033/4915/55812/1191/5654/10631/2152/2697/7043/2952/6935/2200/3572/7177/7031/3479/2006/10451/9370/771/3117/125/652/4693/5346/1524
## DOID:5614              3082/5914/2878/4153/3791/23247/1543/80184/6750/1958/2098/7450/596/9187/2034/482/948/1490/1280/3931/5737/4314/4881/2261/3426/187/629/6403/7042/6785/7507/2934/5176/4060/1277/7078/5950/2057/727/10516/4311/2247/1295/358/10203/2192/582/10218/57125/3485/585/1675/6310/2202/4313/2944/4254/3075/1501/2099/3480/4653/6387/3305/1471/857/4016/1909/4053/6678/1296/7033/4915/55812/1191/5654/10631/2152/2697/7043/2952/6935/2200/3572/7177/7031/3479/2006/10451/9370/771/3117/125/652/4693/5346/1524
## 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

1.3 gseNCG fuction

ncg <- 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
## lung         lung        lung     173      -0.3880662 -1.593961 0.01408451
## breast     breast      breast     133      -0.4869070 -1.889105 0.01492537
## lymphoma lymphoma    lymphoma     188       0.2999589  1.332643 0.03571429
##            p.adjust    qvalues rank                   leading_edge
## lung     0.04477612 0.02356638 2775 tags=31%, list=22%, signal=25%
## breast   0.04477612 0.02356638 2930 tags=33%, list=23%, signal=26%
## lymphoma 0.07142857 0.03759398 2087 tags=21%, list=17%, signal=18%
##                                                                                                                                                                                                         core_enrichment
## 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
## breast                                                                                   KMT2A/ERBB3/SETD2/ARID1A/GPS2/NCOR1/RB1/MAP2K4/NF1/TP53/PIK3R1/STK11/CDKN1B/PTGFR/APC/CCND1/TRAF5/MAP3K1/ESR1/TBX3/FOXA1/GATA3
## 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/PABPC1

1.4 gseDGN fuction

dgn <- 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:C1272641 umls:C1272641 Systemic arterial pressure     318
## umls:C0032914 umls:C0032914              Pre-Eclampsia     334
## umls:C0338656 umls:C0338656         Impaired cognition     342
##               enrichmentScore       NES     pvalue   p.adjust    qvalues
## umls:C1272641      -0.3277208 -1.483240 0.01190476 0.09929391 0.05481488
## umls:C0032914      -0.3066475 -1.390130 0.01219512 0.09929391 0.05481488
## umls:C0338656      -0.3266625 -1.474838 0.01219512 0.09929391 0.05481488
##               rank                   leading_edge
## umls:C1272641 1758 tags=23%, list=14%, signal=20%
## umls:C0032914 1909 tags=29%, list=15%, signal=25%
## umls:C0338656 1997 tags=23%, list=16%, signal=20%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           core_enrichment
## umls:C1272641                                                                                              HGF/SERPINA3/SCNN1A/HECTD4/MBL2/CYP1A1/ENOX1/STK39/INSR/IGFBP7/TENM4/ITGAV/CRBN/FHIT/PRKG1/CD36/CTGF/GULP1/SLC24A3/NPY2R/MMP3/AHR/LONRF1/FBXL7/MEF2C/RPS6KA2/ID4/CDADC1/TGFBR2/VTCN1/CYP7A1/CITED2/SCNN1G/FAM155A/NEFL/JAM2/RGS17/PPARG/CDH13/SYNE1/GRK4/LPL/TRPS1/SEMA5A/KL/UCP1/HSD17B4/KCNMA1/PRLR/CDH11/SORL1/SGCD/NOV/ESR1/IGF1R/ZNF652/ACBD4/FGF18/FRY/ZNF385D/AGTR1/TNFRSF11B/IRS1/DCN/CARTPT/ADRA2A/TBX3/ADIPOQ/CORIN/ADH1B/NOVA1/LRP2
## 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

2 Visualization

References

1. 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).

2. S., A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. biorxiv doi:10.1101/060012

3. Yu, G., Wang, L.-G., Yan, G.-R. & He, Q.-Y. DOSE: An r/bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609 (2015).