The goal of {HarmonizomeR} is to provide a fast interface to download
and perform functional and gene set enrichment analysis from the
Harmonizome database (Rouillard
et al. 2016).
You can install the development version of {HarmonizomeR} from
GitHub with:
# install.packages("devtools")
devtools::install_github("marceelrf/HarmonizomeR")The function show_dataset_collection() is used to check all available
datasets.
show_dataset_collection()
#> To check the complete dataset information,
#> go to: https://maayanlab.cloud/Harmonizome/
#> # A tibble: 130 x 2
#> Name Code
#> <chr> <chr>
#> 1 Achilles Cell Line Gene Essentiality Profiles achi~
#> 2 Allen Brain Atlas Adult Human Brain Tissue Gene Expression Profiles brai~
#> 3 Allen Brain Atlas Adult Mouse Brain Tissue Gene Expression Profiles brai~
#> 4 Allen Brain Atlas Developing Human Brain Tissue Gene Expression Profil~ brai~
#> 5 Allen Brain Atlas Developing Human Brain Tissue Gene Expression Profil~ brai~
#> 6 Allen Brain Atlas Prenatal Human Brain Tissue Gene Expression Profiles brai~
#> 7 BIND Biomolecular Interactions bind
#> 8 BioGPS Cell Line Gene Expression Profiles biog~
#> 9 BioGPS Human Cell Type and Tissue Gene Expression Profiles biog~
#> 10 BioGPS Mouse Cell Type and Tissue Gene Expression Profiles biog~
#> # ... with 120 more rowsLet’s use the SILAC Phosphoproteomics Signatures of Differentially
Phosphorylated Proteins for Drugs
dataset
using the code silacdrug in the function get_geneset().
CAUTION: Depending on the size of your data set and your internet connection this step could take a long time!!!
silacdrug_ds <- get_geneset(code = "silacdrug")
#> Carregando pacotes exigidos: tibble
#> Carregando pacotes exigidos: tidyr
#> Carregando pacotes exigidos: purrr
#> Carregando pacotes exigidos: httr
#> Carregando pacotes exigidos: jsonlite
#>
#> Attaching package: 'jsonlite'
#> The following object is masked from 'package:purrr':
#>
#> flatten
#>
#> =>----------------------------- 4% | ETA: 44s ===>---------------------------
#> 9% | ETA: 26s ====>-------------------------- 13% | ETA: 21s
#> =====>------------------------- 17% | ETA: 21s =======>-----------------------
#> 22% | ETA: 18s ========>---------------------- 26% | ETA: 15s
#> =========>--------------------- 30% | ETA: 13s ==========>--------------------
#> 35% | ETA: 12s ============>------------------ 39% | ETA: 11s
#> =============>----------------- 43% | ETA: 11s ==============>----------------
#> 48% | ETA: 10s ================>-------------- 52% | ETA: 9s
#> =================>------------- 57% | ETA: 8s ==================>------------
#> 61% | ETA: 7s ====================>---------- 65% | ETA: 6s
#> =====================>--------- 70% | ETA: 5s ======================>--------
#> 74% | ETA: 4s =======================>------- 78% | ETA: 4s
#> =========================>----- 83% | ETA: 3s ==========================>----
#> 87% | ETA: 2s ===========================>--- 91% | ETA: 1s
#> =============================>- 96% | ETA: 1shead(silacdrug_ds)
#> # A tibble: 6 x 3
#> Code name symbol
#> <chr> <chr> <chr>
#> 1 silacdrug 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SI~ CEBPZ
#> 2 silacdrug 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SI~ CHD1
#> 3 silacdrug 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SI~ ZDHHC5
#> 4 silacdrug 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SI~ PLCH1
#> 5 silacdrug 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SI~ SIK3
#> 6 silacdrug 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SI~ GRB7To perform the ORA the function EnrichHarmonizome() uses the
{clusterProfiler} package (Wu et al. 2021).
genes <- c("CYP2D26","NCOA7","CCDC3","SNTG2","LIMK1","PPWD1","2900055J20RIK","GM839","HSPA12A","MTIF3","KDM2B",
"FAM221A","GM19710","CCDC68","CNRIP1","GM7544","LGI2","CLIP3","GM9484","1700034J05RIK","RIPK2","DPF2","RPS6KA4","RUNX1","DNM1L","SGTA","PIP5K1B","MTA1","KIAA1524","NCOR2",
"HSP90AB1","ARFIP2","DKC1","KMT2A","RPLP2","PLEC","HSP90AA1","PEAK1","ZDHHC5","TBC1D25")
ORA <- EnrichHarmonizome(gene = genes,
tbl = silacdrug_ds,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
minGSSize = 5,
maxGSSize = 5000)
#> Carregando pacotes exigidos: clusterProfiler
#>
#> Registered S3 method overwritten by 'ggtree':
#> method from
#> identify.gg ggfun
#> clusterProfiler v4.2.2 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
#>
#> If you use clusterProfiler in published research, please cite:
#> T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
#>
#> Attaching package: 'clusterProfiler'
#> The following object is masked from 'package:purrr':
#>
#> simplify
#> The following object is masked from 'package:stats':
#>
#> filter
head(ORA@result)
#> ID
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> Description
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> GeneRatio
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4/23
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4/23
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4/23
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4/23
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 15/23
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 3/23
#> BgRatio
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 91/2770
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 93/2770
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 109/2770
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 115/2770
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 1182/2770
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 85/2770
#> pvalue
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.005979257
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.006460083
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.011265667
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.013542170
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.024166257
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.031710728
#> p.adjust
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.06783087
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.06783087
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.07109639
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.07109639
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.10149828
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.10400006
#> qvalue
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.04760061
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.04760061
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.04989221
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.04989221
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.07122686
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 0.07298250
#> geneID
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs RIPK2/DPF2/RPLP2/HSP90AA1
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs RIPK2/HSP90AB1/RPLP2/HSP90AA1
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs RIPK2/RPS6KA4/PIP5K1B/RPLP2
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs RIPK2/RPS6KA4/PIP5K1B/RPLP2
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs DPF2/RPS6KA4/DNM1L/SGTA/NCOR2/HSP90AB1/ARFIP2/DKC1/KMT2A/RPLP2/PLEC/HSP90AA1/PEAK1/ZDHHC5/TBC1D25
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs RIPK2/DPF2/HSP90AA1
#> Count
#> HS+LS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4
#> HS+LS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4
#> HS_10min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4
#> HS_90min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 4
#> 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 15
#> HS+LS_3min_LPS vs ctrl_RAW264.7_macrophage (Mouse) [20222745]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs 3To perform the GSEA analysis is necessary to convert the geneset to a
named list. The function geneset_to_list() handle with this task.
The function GSEAHarmonizome() uses the {fgsea} package to compute
the GSEA algorithm (Korotkevich, Sukhov, and Sergushichev 2019;
Subramanian et al. 2005; Mootha et al. 2003).
pathways_list <- geneset_to_list(tbl = silacdrug_ds)
length(pathways_list[[1]])
#> [1] 1182
pathways_list[1]
#> $`10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs`
#> [1] "CEBPZ" "CHD1" "ZDHHC5" "PLCH1" "SIK3" "GRB7"
#> [7] "GIT1" "NOC2L" "NUP214" "AFTPH" "SUGP1" "INTS12"
#> [13] "DKC1" "CDC42EP5" "TAF3" "EHBP1L1" "DDX1" "CCNL2"
#> [19] "SMN1" "SRSF10" "RAB11FIP1" "YWHAZ" "N4BP2L2" "SORBS2"
#> [25] "FAM122A" "CHEK1" "EIF2S2" "NUSAP1" "KIF20B" "KDM1A"
#> [31] "RFX7" "IRF2BP1" "STK3" "EGFR" "PRRC2A" "VAPB"
#> [37] "EMD" "RPL30" "NFRKB" "PFKFB3" "MLX" "RPLP2"
#> [43] "MFAP1" "GBF1" "HIST1H1C" "NFX1" "UBA52" "TDP1"
#> [49] "EIF4ENIF1" "PPFIA1" "FAM195B" "NHSL1" "IRF2BPL" "PLXNB3"
#> [55] "G3BP1" "TSC22D4" "C12ORF45" "TRIOBP" "THRAP3" "LIMD1"
#> [61] "PTPRJ" "ATG16L1" "CHTF18" "CTNNA1" "WDR55" "TRIP10"
#> [67] "KAT7" "MCM4" "CYSRT1" "UBE2J1" "ESYT2" "PCBP2"
#> [73] "IRS1" "PLEC" "EPB41L1" "MTHFD1" "SH3KBP1" "CHAMP1"
#> [79] "MYH10" "ARHGAP6" "LUZP1" "LBR" "TAOK1" "SIK2"
#> [85] "EPHA2" "CTAGE5" "PUM2" "TMEM176B" "SNX16" "EAF1"
#> [91] "RANBP3" "TCF4" "C17ORF49" "RPS2" "RPL13" "UTP15"
#> [97] "CGNL1" "RANBP2" "C7ORF43" "RAB7A" "HDAC4" "SHC1"
#> [103] "FNBP1L" "FAM208A" "C5ORF30" "C2CD5" "CLASP1" "TCIRG1"
#> [109] "DBNL" "UHRF1BP1L" "GRASP" "ZNF462" "HDGFRP2" "BCLAF1"
#> [115] "NMT2" "MED24" "INCENP" "YBX3" "ACTC1" "HSP90AB1"
#> [121] "KIAA1522" "YY1" "ZWINT" "CLUAP1" "TWISTNB" "BAG3"
#> [127] "BIN1" "WNK1" "TRAPPC8" "CCDC92" "PJA2" "UBR4"
#> [133] "SPIDR" "WASF2" "GMEB2" "AMPD2" "HNRNPLL" "SKA3"
#> [139] "MXRA8" "ABI1" "LARP1" "RANBP9" "ASAP1" "CHFR"
#> [145] "SLC1A5" "PSMD11" "PLEKHG3" "SAMD4B" "MYBBP1A" "ZNF569"
#> [151] "SGTA" "CRIP2" "RAB9A" "CDK1" "MTFR1L" "EXOC1"
#> [157] "CDC42EP4" "LIG3" "EPHB4" "ITPR3" "DTD1" "EIF3B"
#> [163] "YTHDF1" "ADD1" "CEP55" "SPAG9" "LTV1" "BMS1"
#> [169] "EIF2B5" "FAM160B1" "ZNF598" "BRWD1" "KRT8" "DOPEY1"
#> [175] "TLE6" "FAM175A" "SF3B2" "PDS5B" "UBE2T" "STIM1"
#> [181] "IKBKB" "RSRC2" "AGAP1" "TRAF3IP1" "HN1L" "ANP32B"
#> [187] "ERCC6L" "HEATR3" "ARGLU1" "DOCK1" "SPECC1L" "GIGYF2"
#> [193] "ARL6IP4" "RNF187" "CKAP2" "CYLD" "UBAP2" "CLUH"
#> [199] "BAIAP2L1" "LSR" "RACGAP1" "YAP1" "TRAM1" "NELFE"
#> [205] "CDC37" "PPP2R5D" "KLC3" "NEO1" "HSP90AA1" "ACIN1"
#> [211] "MBD5" "USP15" "PELP1" "PA2G4" "POLR2A" "INPP5E"
#> [217] "TSC22D2" "TMCC2" "PRKRA" "APRT" "SPEN" "CAMSAP1"
#> [223] "CFDP1" "SMARCC1" "OSBP" "BRAF" "CHD4" "RIF1"
#> [229] "UBA1" "QARS" "MFF" "PSIP1" "HJURP" "VPRBP"
#> [235] "STON1" "REXO1" "ACACA" "TNKS1BP1" "PAK2" "RAPGEF3"
#> [241] "C8ORF33" "GOLGA3" "IFIH1" "ALKBH5" "EIF4G3" "TRA2B"
#> [247] "GPHN" "LMNB1" "CDK18" "DUSP16" "WDR20" "MARS"
#> [253] "IVNS1ABP" "ITGB4" "POLDIP3" "KLF16" "FAM193A" "RBMX"
#> [259] "RFX1" "GOLM1" "XPO4" "KIF3A" "MTMR3" "CHD2"
#> [265] "TPX2" "ZNF330" "PDIA6" "ZNF503" "ARHGAP32" "RNASEH2A"
#> [271] "WHSC1" "CBL" "DGKQ" "RBM7" "PKN2" "ARFIP1"
#> [277] "ATF2" "TLK1" "CDV3" "LMO7" "EIF3C" "CACTIN"
#> [283] "ATXN2" "PPP1R12A" "HP1BP3" "CBX4" "GNL3" "SSFA2"
#> [289] "CRTC2" "SET" "USP48" "SSH3" "RB1CC1" "MAPK3"
#> [295] "BAZ2A" "CAPZA2" "NELFA" "NT5C3A" "TFE3" "RPL24"
#> [301] "PPP1R12C" "STMN1" "LEMD3" "BAD" "ZCCHC9" "MAP3K2"
#> [307] "WDR43" "HLA-A" "EML3" "CASP8AP2" "VIM" "TOM1"
#> [313] "SIPA1L1" "CRTC3" "TMEM106B" "MAP4" "MATR3" "SLC4A1AP"
#> [319] "ZCCHC6" "BCL2L13" "RPTOR" "PIKFYVE" "PWWP2B" "HCFC1"
#> [325] "FKBP3" "CCDC43" "SLC4A4" "VPS51" "DOCK7" "PAN3"
#> [331] "RAVER1" "CFL2" "HELLS" "SPATS2" "RHBDF2" "PICK1"
#> [337] "SPATA13" "NMNAT1" "SALL1" "MYC" "TRIP6" "F11R"
#> [343] "SNRNP200" "RBM33" "DNAJC5" "KSR1" "LVRN" "DYNC1LI1"
#> [349] "PEX19" "SGSM3" "SAP30BP" "KIF20A" "RIPK1" "SNAP23"
#> [355] "NEDD4" "UBAP2L" "LEMD2" "DDX27" "TFEB" "CENPV"
#> [361] "CASC3" "SLC7A7" "HMGA1" "DDX20" "CSNK1E" "ING4"
#> [367] "MTF1" "SH3BP1" "CSTF3" "FAM104A" "HMGXB4" "CALM2"
#> [373] "TCEB3" "ZC3HAV1" "FAM92A1" "EIF4H" "LIMK2" "ESF1"
#> [379] "PPP4R2" "SMARCA4" "ZC3H13" "CXORF23" "EPB41" "SQSTM1"
#> [385] "WBP11" "KDM3B" "ARFGAP2" "SLC26A2" "NOP56" "SPHK2"
#> [391] "ARHGDIA" "ANAPC1" "TRIM28" "HSPA4" "COL4A3BP" "JADE3"
#> [397] "CD2BP2" "EI24" "NBN" "POLA2" "FAM63A" "ARHGAP5"
#> [403] "LARP4" "EIF4EBP1" "OTUD7B" "NOM1" "SMAP2" "CFL1"
#> [409] "DST" "SLTM" "C2ORF44" "AMPD3" "SETD2" "GPRC5C"
#> [415] "CCNK" "MIER3" "CDKN2AIP" "DNTTIP2" "DPYSL2" "CTTNBP2NL"
#> [421] "IFITM3" "DNM1L" "UGDH" "ITPKB" "PEAK1" "PARD3"
#> [427] "TRIP12" "TOP2A" "PRPF40A" "PDCL3" "SLC35C2" "NCOA5"
#> [433] "TRMT1" "IWS1" "NUFIP2" "CKAP5" "SPRED1" "RPS10"
#> [439] "LIPE" "FAM160A2" "CXADR" "MAP2K4" "ARRB1" "NUDC"
#> [445] "TBC1D1" "ZNF7" "TBC1D4" "ADD3" "NF1" "BET1"
#> [451] "IFI35" "THUMPD1" "ZNF687" "SLC23A2" "EPN3" "ZMYM2"
#> [457] "MED1" "EDC4" "ETV3" "RRP1" "LTBP4" "CASP7"
#> [463] "FOSL2" "PVRL1" "LMNA" "SPTBN1" "ABI2" "PRKD2"
#> [469] "C11ORF58" "ANKRD17" "HN1" "MLF2" "PUS10" "REEP3"
#> [475] "RRP9" "CTR9" "KANK2" "SDC1" "FBXW8" "CLSPN"
#> [481] "BAIAP2" "PBX2" "GPSM2" "MAPK1" "SIK1" "MAPK14"
#> [487] "HIRIP3" "GIGYF1" "AATF" "PUM1" "TTI1" "PDCD4"
#> [493] "NUMA1" "TCF20" "RTKN" "SSBP3" "GORASP2" "MAPK10"
#> [499] "RNF168" "PSMF1" "EIF2AK3" "PRR12" "MOB1B" "C12ORF10"
#> [505] "LUC7L" "TRAPPC10" "BOD1L1" "CRK" "YBX1" "SRSF11"
#> [511] "CAP1" "DLGAP5" "NIPBL" "RBM3" "IGHMBP2" "PLEKHM1"
#> [517] "ZNF106" "COBLL1" "DTX2" "TULP3" "MDN1" "ARID4A"
#> [523] "BIRC6" "BSG" "LLPH" "STK4" "NCL" "PLEKHA5"
#> [529] "ERF" "SNX5" "SRPK2" "CTDP1" "KRT20" "NOL9"
#> [535] "FBL" "LMBRD2" "PSMD4" "FTSJ3" "PEX1" "RPL6"
#> [541] "KNOP1" "EIF3D" "DDX42" "HSF1" "TOP1" "STRN4"
#> [547] "SLC16A10" "SRGAP2" "STAMBPL1" "WIPF2" "ZRANB3" "HYPK"
#> [553] "LARP7" "MAST4" "ARPC1B" "DIP2B" "GAB1" "PITPNB"
#> [559] "TPR" "RPL7" "RBM39" "FBXO30" "TNS2" "PRKAB2"
#> [565] "SRP14" "ETV6" "CUX1" "CDCA2" "KIF2C" "ARHGEF10L"
#> [571] "MCM2" "KIF23" "NFIC" "SIN3A" "HNRNPAB" "MELK"
#> [577] "SLC20A2" "ZRANB2" "NUP35" "MAP4K4" "STRN3" "STRIP1"
#> [583] "FEZ2" "RPL37" "TAGLN2" "MCM6" "EPS15" "KIF4A"
#> [589] "PABPN1" "TSC2" "RBM25" "CDCA3" "ENSA" "MYH9"
#> [595] "SRRT" "UBE2O" "POLA1" "LGR5" "SUPT6H" "C10ORF88"
#> [601] "TOR1AIP1" "KIF5B" "FLNA" "HCK" "SUN1" "ZW10"
#> [607] "NPAT" "HNRNPU" "PLCB3" "ARHGEF11" "DENND5B" "APBB2"
#> [613] "SNTB1" "SCAF4" "NCAPH" "BCAR1" "HNF1B" "PCNP"
#> [619] "LRRFIP2" "CBX8" "SRSF2" "RPS6KA4" "PAPOLA" "INO80C"
#> [625] "TSR3" "PSD4" "ARHGEF28" "MAGI3" "SDC2" "KLC1"
#> [631] "RNF8" "MINK1" "CIC" "PIGB" "ZC3HC1" "AARSD1"
#> [637] "R3HDM2" "SNTB2" "SHROOM3" "GCFC2" "SNX2" "BCL9L"
#> [643] "RAB3GAP1" "MUS81" "NUP153" "ALAD" "NFKB1" "RIN2"
#> [649] "TJAP1" "BNIP2" "NIFK" "FARP1" "KLC2" "UTP18"
#> [655] "ZZZ3" "FAF1" "IQSEC1" "BCR" "ZFR" "AKT1"
#> [661] "TUBA1B" "ACLY" "ARMC1" "MEPCE" "SCRIB" "TMEM245"
#> [667] "DEPTOR" "MORC3" "NEDD4L" "RBL1" "AEBP2" "ROCK2"
#> [673] "CDC26" "PML" "PEA15" "UACA" "SH3BP4" "NUP133"
#> [679] "MKI67" "RBM10" "RNMT" "AKIRIN2" "SPRY4" "AKAP13"
#> [685] "KIFAP3" "TMEM63B" "DMXL1" "ARFIP2" "MARK2" "SMARCAD1"
#> [691] "RPRD2" "HMHA1" "VTI1B" "LPP" "NAV1" "PRPF38A"
#> [697] "DCP2" "TAB2" "MTSS1" "HTATSF1" "TICRR" "PRPF4B"
#> [703] "OTUD4" "EPB41L2" "DHX16" "PARD6B" "RBM17" "RBM6"
#> [709] "MLLT6" "SPTAN1" "MPHOSPH10" "HNRNPA2B1" "ZZEF1" "BRD8"
#> [715] "NCOA3" "CGN" "EPS8L2" "MKL2" "SYNE2" "USP10"
#> [721] "RAB3IP" "MED15" "PCGF6" "AAK1" "KIAA1468" "RBM15"
#> [727] "KIAA0430" "FRA10AC1" "ZBED9" "EIF4B" "LRRC8A" "MRE11A"
#> [733] "KIAA1598" "SNX17" "HDGF" "SF3A1" "RSF1" "REPS1"
#> [739] "TMOD3" "CBX3" "SLC9A6" "PCIF1" "AGFG1" "PHAX"
#> [745] "ARHGAP1" "SSB" "SMEK1" "RAD18" "VAMP8" "USP4"
#> [751] "SPIRE2" "PAWR" "ABL2" "NOP2" "MCRS1" "RAB11FIP5"
#> [757] "LASP1" "STK24" "SDPR" "TNS1" "PNISR" "CASP3"
#> [763] "ZUFSP" "FRMD4B" "CNPY4" "PHLDB2" "DLGAP4" "SLC4A7"
#> [769] "METAP2" "NCOR1" "HNRNPH1" "IRF2BP2" "TMEM230" "URI1"
#> [775] "VCL" "KMT2A" "SUFU" "CES5A" "STXBP5" "NUP205"
#> [781] "RABEP1" "NCAPD2" "RPS6KA1" "BMI1" "ZFC3H1" "LRRC41"
#> [787] "USP39" "GPN1" "TCEAL5" "JUN" "MLLT4" "NUCKS1"
#> [793] "FLNC" "SH3PXD2B" "NECAP2" "MYO9B" "ZC3H11A" "NOL4L"
#> [799] "DENND4C" "GAPVD1" "TRIM24" "ZNF608" "HDAC6" "COIL"
#> [805] "SORD" "SLC33A1" "DNMT1" "FAM91A1" "HNRNPC" "FAM83H"
#> [811] "NPM3" "CD44" "SMCR8" "GTF3C4" "CTTN" "CHAF1B"
#> [817] "VPS26B" "TRIP11" "TRA2A" "PRUNE2" "INTS6" "NUFIP1"
#> [823] "C2ORF49" "ZAK" "CAMK2G" "TRIO" "SRRM2" "RFTN1"
#> [829] "API5" "SAV1" "SEC22B" "NPM1" "OPTN" "ALS2"
#> [835] "EEF2" "APOBR" "SRM" "CTIF" "PLEKHA6" "NVL"
#> [841] "EIF1" "HNRNPK" "U2AF2" "RRAD" "SLC16A1" "ZFHX3"
#> [847] "UBR5" "DOCK5" "CDC5L" "ATAD2" "KANK1" "IRS2"
#> [853] "GOLGB1" "TMSB10" "FAM195A" "ZC3H8" "ZFP36L1" "NDRG3"
#> [859] "MIA3" "FHOD1" "CAMSAP2" "RFC1" "BCKDHA" "CPD"
#> [865] "TNS3" "ATG2B" "PPP1R18" "UCK2" "PHF14" "CEP97"
#> [871] "MAVS" "SMG9" "CCT8" "MAP1S" "KANSL1" "TJP2"
#> [877] "CBX5" "TXLNA" "ESAM" "YWHAE" "IRF3" "PRPF3"
#> [883] "AKT1S1" "PHACTR4" "BICC1" "PHC3" "TTC7A" "RNF20"
#> [889] "NUP188" "ATRX" "KIF21A" "KIAA1715" "IBTK" "TOX4"
#> [895] "RPS20" "PLEKHA7" "EIF4G1" "SUPT5H" "BCL9" "JAG1"
#> [901] "ZC3H14" "PSRC1" "CPEB4" "UFL1" "PRKD3" "CDK17"
#> [907] "C1ORF52" "ARMC10" "RBBP6" "RABEPK" "MAST2" "BRCA1"
#> [913] "FLRT1" "DAXX" "FAM76B" "CCDC88A" "DPF2" "PDZD8"
#> [919] "PTPN12" "SUB1" "ATXN2L" "SMARCC2" "STK11IP" "RANGAP1"
#> [925] "TLK2" "NUP50" "EML4" "GRAMD3" "UBAP1" "NASP"
#> [931] "RBM8A" "NOLC1" "GINS2" "STRN" "EPS15L1" "PTPRG"
#> [937] "LRRFIP1" "NSFL1C" "FXR1" "SAFB2" "DIDO1" "MED14"
#> [943] "ULK1" "ARHGAP21" "MKL1" "PPP1R10" "PLEKHG2" "CLIP1"
#> [949] "SYNRG" "ZNF281" "CDK12" "ETHE1" "ELF1" "GOLGA5"
#> [955] "PTPN21" "APLF" "POF1B" "TP53BP1" "MDC1" "SNX7"
#> [961] "LSM14B" "PLEKHA1" "TP53BP2" "SF3B1" "TMPO" "PAXBP1"
#> [967] "SLMAP" "LRP1" "CD2AP" "FKBP15" "PEX14" "CNPY2"
#> [973] "GATAD2B" "DSP" "EIF5B" "NUCB1" "ZCCHC11" "OSBPL11"
#> [979] "RSL1D1" "ARHGEF40" "TMEM55A" "METTL1" "RPS6KA3" "PHLDB1"
#> [985] "SYNPO" "PDLIM7" "BNIP3" "RASA3" "DNAJC2" "SLIRP"
#> [991] "ARFGAP1" "RAD51AP1" "ZMYM4" "MAP3K3" "WIBG" "CNOT3"
#> [997] "PPIL4" "CTNND1" "KPNA3" "HNRNPUL2" "NCBP1" "DUSP7"
#> [1003] "TRAF7" "LYSMD2" "FOXK1" "SMTN" "XRCC6" "MYO18A"
#> [1009] "MBD3" "SBNO1" "NEK9" "ABCF1" "PPFIBP1" "HMGN1"
#> [1015] "AKAP10" "TRIM2" "KRT18" "TAB3" "RICTOR" "MDH1"
#> [1021] "PRRC2C" "RBM14" "HNRNPA1" "ARFGEF1" "CKAP4" "PNN"
#> [1027] "CDK4" "MRPL23" "NME2" "ASUN" "HS1BP3" "GMIP"
#> [1033] "ZNRF2" "BRD7" "ZYX" "SH2B1" "PCM1" "EIF6"
#> [1039] "PRRC2B" "DDX46" "MYO1E" "MTDH" "PPIP5K2" "SLC9A3R1"
#> [1045] "SFR1" "GTSE1" "SERBP1" "PRKAB1" "CNOT2" "PJA1"
#> [1051] "RIN1" "NR2C2AP" "WDR62" "CHERP" "NAB2" "ELMSAN1"
#> [1057] "CTNNB1" "CEP170" "GFPT2" "WDR70" "SOS1" "AHCTF1"
#> [1063] "ARHGEF7" "CCNT1" "PPHLN1" "MAPRE2" "PCBP1" "PXN"
#> [1069] "UBA2" "ETL4" "ASPSCR1" "MAP7D1" "MARK3" "ARFGEF2"
#> [1075] "SRA1" "SLAIN2" "ADRBK1" "NUP93" "HSPB8" "YTHDC2"
#> [1081] "THUMPD3" "SMG6" "NMT1" "UBXN7" "TFIP11" "AP3B1"
#> [1087] "EMG1" "NOL10" "KLHL6" "ADNP" "DDX55" "NCOR2"
#> [1093] "CEP170B" "PDAP1" "HAUS6" "TCOF1" "LYSMD3" "ZMYND8"
#> [1099] "TRRAP" "ERBB2IP" "ATF7" "ASAP2" "CAST" "RPS17"
#> [1105] "PDXDC1" "SRRM1" "PPP1R13L" "FAT1" "HDLBP" "NSUN2"
#> [1111] "TTLL12" "SPRY1" "ERRFI1" "PLCG1" "MARCKS" "CHD8"
#> [1117] "PKP4" "RAI14" "CDK16" "PRKAA1" "PID1" "TFAP4"
#> [1123] "MTUS2" "FAM207A" "MTA3" "RALGAPA2" "IFITM2" "SLC38A2"
#> [1129] "PFAS" "EIF4A3" "CDC42EP1" "NMD3" "LRP4" "PTPN1"
#> [1135] "TJP1" "TMEM115" "TBC1D25" "RTN4" "CWC27" "TXNL1"
#> [1141] "NFATC2IP" "SOD1" "LEO1" "ZCCHC8" "CC2D1B" "TPD52L2"
#> [1147] "RPS3" "FXR2" "HNRNPA3" "SVIL" "C19ORF47" "SLK"
#> [1153] "ARHGAP35" "FNBP4" "MCM3" "SNIP1" "EIF3J" "WDHD1"
#> [1159] "RALY" "DSTN" "LATS1" "CCNL1" "TBX3" "PALLD"
#> [1165] "GTF2F1" "PURB" "WIZ" "DDX51" "WDR75" "PPP6R3"
#> [1171] "NUP98" "RPRD1B" "DMWD" "AHNAK" "SFSWAP" "SH3GL1"
#> [1177] "ABLIM1" "MTUS1" "PTMA" "TAF7" "PTPRC" "LUC7L2"data("example_gsea")
GSEA <- GSEAHarmonizome(pathways = pathways_list,
stats = gene_ranks,minSize = 10,maxSize = 500)
#> Carregando pacotes exigidos: fgsea
#> Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.09% of the list).
#> The order of those tied genes will be arbitrary, which may produce unexpected results.
#> Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize,
#> gseaParam, : There are duplicate gene names, fgsea may produce unexpected
#> results.
head(GSEA)
#> pathway
#> 1: 10min_PPase_inhibitors vs ctrl_Hepa1-6 (Mouse) [18846507]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 2: 10uM_erlotinib vs ctrl_KG-1 (Human) [22115753]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 3: 10uM_gefitinib vs ctrl_KG-1 (Human) [22115753]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 4: 4h_cisplatin vs ctrl_mESC (Mouse) [22006019]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 5: 50nM_dasatinib vs ctrl_K562 (Human) [19651622]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> 6: 5nM_dasatinib vs ctrl_K562 (Human) [19651622]/SILAC Phosphoproteomics Signatures of Differentially Phosphorylated Proteins for Drugs
#> pval padj log2err ES NES size
#> 1: 5.617321e-34 1.291984e-32 1.51610760 0.4545874 2.1242699 1049
#> 2: 8.757515e-01 8.757515e-01 0.05184576 -0.3858994 -0.6638719 3
#> 3: 8.757515e-01 8.757515e-01 0.05184576 -0.3858994 -0.6638719 3
#> 4: 1.678465e-12 9.651174e-12 0.91011973 0.4320662 1.9209053 441
#> 5: 6.375163e-04 1.332989e-03 0.47727082 0.4375812 1.6539977 104
#> 6: 6.193414e-03 9.039041e-03 0.40701792 0.4170298 1.5294317 88
#> leadingEdge
#> 1: LIPE,RPRD2,ZW10,LVRN,ARFGEF2,ZYX,...
#> 2: CTDSPL2,PTPN6,SYK
#> 3: PTPN6,COPS8,SYK
#> 4: MCM10,RRP7A,ZW10,KIDINS220,JAM2,RFX2,...
#> 5: RPUSD2,PTRH1,TNK1,TTC25,PRKCB,SAFB,...
#> 6: RPUSD2,PTRH1,PRKCB,RET,RCSD1,TNIK,...GSVAHarmonizome() wraps the {GSVA} package to perform the GSVA
algorithm (Hänzelmann, Castelo, and Guinney 2013; Hanzelmann, Castelo,
and Guinney 2013).
data("example_gsva")
GSVA <- GSVAHarmonizome(expr = example_expr,pathways = pathways_list)
#> Carregando pacotes exigidos: GSVA
#> Estimating GSVA scores for 23 gene sets.
#> Estimating ECDFs with Gaussian kernels
#> | | | 0% | |=== | 4% | |====== | 9% | |========= | 13% | |============ | 17% | |=============== | 22% | |================== | 26% | |===================== | 30% | |======================== | 35% | |=========================== | 39% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 61% | |============================================== | 65% | |================================================= | 70% | |==================================================== | 74% | |======================================================= | 78% | |========================================================== | 83% | |============================================================= | 87% | |================================================================ | 91% | |=================================================================== | 96% | |======================================================================| 100%The authors thanks FAPESP(n 2018/05731-7) for the funding.
Hanzelmann, Sonja, Robert Castelo, and Justin Guinney. 2013. “GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data” 14: 7. https://doi.org/10.1186/1471-2105-14-7.
Hänzelmann, Sonja, Robert Castelo, and Justin Guinney. 2013. “GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data.” BMC Bioinformatics 14 (1). https://doi.org/10.1186/1471-2105-14-7.
Korotkevich, Gennady, Vladimir Sukhov, and Alexey Sergushichev. 2019. “Fast Gene Set Enrichment Analysis.” https://doi.org/10.1101/060012.
Mootha, Vamsi K, Cecilia M Lindgren, Karl-Fredrik Eriksson, Aravind Subramanian, Smita Sihag, Joseph Lehar, Pere Puigserver, et al. 2003. “PGC-1α-Responsive Genes Involved in Oxidative Phosphorylation Are Coordinately Downregulated in Human Diabetes.” Nature Genetics 34 (3): 267–73. https://doi.org/10.1038/ng1180.
Rouillard, Andrew D., Gregory W. Gundersen, Nicolas F. Fernandez, Zichen Wang, Caroline D. Monteiro, Michael G. McDermott, and Avi Ma’ayan. 2016. “The Harmonizome: A Collection of Processed Datasets Gathered to Serve and Mine Knowledge about Genes and Proteins.” Database 2016: baw100. https://doi.org/10.1093/database/baw100.
Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, et al. 2005. “Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.” Proceedings of the National Academy of Sciences 102 (43): 15545–50. https://doi.org/10.1073/pnas.0506580102.
Wu, Tianzhi, Erqiang Hu, Shuangbin Xu, Meijun Chen, Pingfan Guo, Zehan Dai, Tingze Feng, et al. 2021. “clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data” 2: 100141. https://doi.org/10.1016/j.xinn.2021.100141.
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