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 enrichmentScore
## DOID:0060084 DOID:0060084 cell type benign neoplasm     439      -0.2837045
## DOID:5614       DOID:5614               eye disease     450      -0.3125247
## DOID:5679       DOID:5679           retinal disease     299      -0.3676313
##                    NES     pvalue   p.adjust    qvalues rank
## DOID:0060084 -1.254662 0.01250000 0.06922399 0.02923977 1890
## DOID:5614    -1.380387 0.01315789 0.06922399 0.02923977 1768
## DOID:5679    -1.591370 0.01315789 0.06922399 0.02923977 1768
##                                leading_edge
## DOID:0060084 tags=23%, list=15%, signal=20%
## DOID:5614    tags=22%, list=14%, signal=19%
## DOID:5679    tags=24%, list=14%, signal=21%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  core_enrichment
## DOID:0060084 37/268/2735/6752/3082/5914/2878/7508/100507436/3791/1301/1543/1027/1028/1958/7173/7201/8743/7450/596/947/2034/11197/4314/324/3964/196/3675/595/6843/573/2246/3912/1902/7507/2308/8829/6817/2934/1277/3953/55384/8773/8991/2057/4311/2247/6414/4588/5243/5468/358/1012/6469/9457/1733/81029/3952/126/79068/7058/4488/1009/4313/56034/3625/2944/6925/2099/3480/6387/5159/57496/1462/1289/2690/6571/4487/1191/54361/10752/5744/4922/2487/247/7043/1811/2952/367/3572/1287/4582/7031/3479/6424/4629/652/10647/5241/4969
## 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:5679                                                                                                                                                   2878/3791/23247/80184/6750/7450/596/9187/2034/482/948/1490/1280/5737/4314/4881/3426/187/629/6403/6785/2934/5176/7078/5950/727/10516/4311/2247/1295/358/10203/582/10218/57125/585/1675/6310/2202/4313/2944/4254/3075/2099/3480/4653/6387/1471/857/4016/1909/4053/6678/1296/4915/55812/1191/5654/10631/2697/2952/6935/2200/3479/2006/10451/9370/771/652/4693/5346/1524

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
## breast     breast      breast     133      -0.4869070 -1.873013 0.01388889
## lung         lung        lung     173      -0.3880662 -1.532406 0.01408451
## lymphoma lymphoma    lymphoma     188       0.2999589  1.240661 0.03571429
##            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 0.07142857 0.03759398 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/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:C0029456 umls:C0029456               Osteoporosis     375
## umls:C0338656 umls:C0338656         Impaired cognition     342
## umls:C1272641 umls:C1272641 Systemic arterial pressure     318
##               enrichmentScore       NES     pvalue   p.adjust    qvalues
## umls:C0029456      -0.3439046 -1.517108 0.01388889 0.09310987 0.05423242
## umls:C0338656      -0.3266625 -1.450775 0.01408451 0.09310987 0.05423242
## umls:C1272641      -0.3277208 -1.444783 0.01408451 0.09310987 0.05423242
##               rank                   leading_edge
## umls:C0029456 1766 tags=23%, list=14%, signal=20%
## umls:C0338656 1997 tags=23%, list=16%, signal=20%
## umls:C1272641 1758 tags=23%, list=14%, signal=20%
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  core_enrichment
## umls:C0029456 HGF/PTH1R/CYP1A1/JAG1/ROR2/FLT3/CUL9/EEF1A2/THSD4/BCL2/ITGAV/WIF1/GREM2/COL15A1/HPGDS/VGLL3/SLIT3/NRIP1/TMEM135/MGP/PLCL1/OSBPL1A/PIBF1/SELP/SPRY1/MMP13/ID4/SPP2/COL1A2/AOX1/ARHGEF3/GSN/TSC22D3/ATP1B1/NR5A2/ANKH/COL1A1/LEPR/THSD7A/GC/FGF2/PPARG/NOX4/ZNF266/GHRH/BHLHE40/SLC19A2/THBD/FLNB/KL/LEP/HSD17B4/CTSK/FTO/MMP2/ESR1/IGF1R/PTN/IRAK3/HSPA1L/CST3/GHR/SPARC/KDM4B/LRP1/INPP4B/BMPR1B/PTHLH/DPT/FRZB/GSTT1/AR/TNFRSF11B/IRS1/WLS/GSTM3/TGFBR3/TPH1/IGF1/SFRP4/CORIN/BMP4/CHAD/FOXA1/PGR
## 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: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

2 Visualization

2.1 cnetplot

cnetplot(ncg, categorySize="pvalue", foldChange=geneList)