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Among the databases and web services currently supported by rbioapi Enrichr, miEAA, PANTHER, Reactome, and STRING offer Enrichment (Over-Representation) analysis API endpoints. In this article, after a brief introduction, we will demonstrate how to perform Enrichment (Over-Representation) analysis in R, consistently and easily, using the package rbioapi.

Before continuing, note that rbioapi is an interface to the databases and web services. Hence it is the user’s responsibility to cite the services they have used. We provide suggested citations in each function’s manual. However, it is the user’s responsibility to fully and adequately cite the databases or web services used in their published research.


How to install?

You can install the stable release version of rbioapi from CRAN with:

install.packages("rbioapi")

However, the CRAN version is released at most once every 1-2 months, You can install the most recent (development) version from GitHub with:

install.packages("remotes")
remotes::install_github("moosa-r/rbioapi")

What is Over-representation analysis?

Enrichment (Over-Representation) analysis is commonly performed when one’s analysis yields a list of genes, proteins, or other entities. We will not delve into details here and refer you to D. W. Huang et al.: Nucleic Acids Res. 37, 1 (2009) for a thorough review of the subject.

In short, to perform such analysis, in addition to some algorithms, two data are required:

  1. A gene list which is derived from your analysis.

  2. Gene sets, where genes are grouped based on a shared property or concept (such as pathway, role in diseases, physical location, etc.).

With over-representation analysis, you can compare your gene list to find if the member of any gene set are over-represented, meaning that they are present in your list in a manner that can not be explained by chance alone.

Enrichment (Over-Representation) analysis in R with rbioapi

Many web services supported by rbioapi provide tools to perform over-representation analysis against the gene sets curated in their databases. Among the databases and web services currently supported by rbioapi, Enrichr, miEAA, PANTHER, Reactome, and STRING provide such services. Each service has a dedicated vignette article in rbioapi. Here we will focus on performing Enrichment (Over-Representation) analysis.

As a demonstration, we will use the results of Galani et al.: Nat. Immunol. 22, 32 (2021) where they have compared the gene expression of critically ill and non-critically ill COVID patients to healthy individuals. We assign a variable with 1214 differentially expressed genes (DEGs) exclusively induced in critically ill COVID patients. Next, we will see what insights we can extract from this list.

covid_critical <- c("TXNDC5", "GABRR2", "MGAM2", "LOC200772", "LYPD2", "IFI27", "RPH3A", "OTOF", "NBPF8", "CLEC4F", "CNGB1", "HIST1H2BF", "HIF1A", "SDC1", "TUBB8", "FBXO39", "TPSB2", "CD177", "LRRN3", "EBLN2", "PCSK9", "ELK2AP", "UCHL1", "C22orf15", "LPO", "C3orf20", "CLRN1-AS1", "GPR75", "CA12", "RAB19", "CHRFAM7A", "CRYGN", "DLGAP5", "BTBD8", "LOC100272216", "PRG3", "CYP46A1", "LOC102723604", "PPAP2B", "C4BPA", "SPESP1", "LILRP2", "UBE2Q1-AS1", "MIR3945", "NOMO3", "MEG3", "LOC400927-CSNK1E", "MIR6732", "MIR590", "PPP1R3G", "PYCR1", "ARHGAP42", "MMP8", "HMMR", "P3H2", "ACER1", "NOG", "RAB39A", "ANTXRLP1", "LINC00266-3", "GPRC5D", "MCM10", "TSPY26P", "ANKRD36BP1", "GBP1P1", "PRL", "CYP1A1", "KIF4A", "LOC102724323", "SERPINB10", "GSTA4", "TRIM51", "MIXL1", "RNASE1", "CASC8", "MAOA", "XCL1", "ADAMTS2", "LOC101929125", "DCANP1", "BHLHA15", "ANOS1", "SLC18A1", "CCDC150", "CAV1", "SH3BP5", "LINC00398", "NCOA2", "SPATC1", "SHROOM2", "GPR27", "LRRC26", "RNF169", "USP3-AS1", "VWA7", "ROCK2", "FSTL4", "METTL7B", "CYP4F29P", "LEF1-AS1", "HORMAD2-AS1", "FBXO15", "PPTC7", "TYMS", "PPP4R2", "ZNF608", "FAM46B", "PCSK1N", "LINC00623", "CASC5", "ZNF224", "DENND2C", "WDR86", "PTGR1", "SPATA3-AS1", "LOC101927412", "KIF14", "MMP28", "PBK", "VMO1", "ADCY3", "HIST1H2BO", "FTO-IT1", "MIR342", "FANK1", "CRIP2", "TIAF1", "LOC344887", "OLFM4", "MKNK1-AS1", "ZDHHC19", "SEPT14", "EPSTI1", "FOXC1", "MIR616", "KRT72", "LINC01347", "LOC101928100", "KIAA0895", "BOK", "HIST1H2AI", "DBH-AS1", "ADORA2A-AS1", "MED12L", "SAMD15", "TARM1", "SMTNL1", "POU5F1P3", "LINC00968", "OAS3", "LOC103091866", "SH3RF3-AS1", "NBPF10", "APAF1", "SLC2A14", "SYT17", "ETV3", "SHROOM4", "AOAH-IT1", "NAIP", "ALAS2", "GLIS3", "ADAM17", "OLFM1", "PCAT29", "TNFRSF18", "DNASE1L3", "IGF2BP3", "LINC01271", "AP3B2", "TXNDC2", "CEP55", "SIGLEC1", "RAB3IL1", "PLD4", "KIFC1", "LINC00487", "ABHD12B", "ITGA7", "GJB6", "CARD14", "LRRN2", "MPO", "KLRC3", "LOC100507487", "SCGB3A1", "CD38", "LRRN1", "SYCP2L", "ANLN", "ASPM", "OAS1", "IFI44L", "CDCA3", "HLA-DQB2", "ANO9", "NUDT11", "HMP19", "DEPDC1", "GPR84", "PLEKHF1", "PI16", "RDH5", "TMED8", "LINC00824", "SEPT4", "CLIC3", "B4GALNT3", "OLAH", "ITGA1", "FOXI1", "LOC100506142", "CDC20", "HAGHL", "GTSCR1", "B3GALNT1", "SOCS3", "PCDH1", "TAS2R20", "CDC25A", "NPDC1", "LOC100505915", "XCL2", "TIAM2", "LOC100288069", "IL34", "IL18R1", "CYYR1", "ZNF888", "FAM20A", "MDS2", "ABCA13", "KCNJ1", "SLC4A9", "EXO1", "LAIR2", "IQSEC3", "SCT", "SLC26A8", "ESCO2", "ZDHHC1", "SNORA63", "FBLN5", "PRUNE2", "CPNE7", "CDHR2", "GNLY", "APOBEC3B", "MFSD9", "SYNGR3", "PTGDS", "COL17A1", "TBC1D14", "AIM2", "TMEM204", "FAM157B", "ZBED6", "EME1", "ATF3", "KBTBD4", "LUC7L2", "KIF20A", "LCNL1", "DOCK9-AS2", "KCNE1", "BTN2A3P", "KL", "IDI2-AS1", "EBF4", "SCARNA21", "CEACAM6", "KLRB1", "C5orf58", "ASXL2", "RPLP0P2", "CYTL1", "DIAPH3", "DOK7", "RFFL", "KIAA1107", "TAS2R40", "CCDC186", "COL6A2", "METRN", "SNHG25", "RRM2", "CYP1B1", "NEURL1", "MATK", "SLC28A3", "JCHAIN", "TNFRSF4", "FXYD1", "PLLP", "ARHGAP23", "SNORA4", "MYO18A", "ZNF429", "NRN1", "HJURP", "TCN1", "CDC6", "ATP5EP2", "SOAT2", "LOC101928034", "EIF2AK2", "ARG1", "SLC16A11", "TPPP3", "TMEM38A", "TOP2A", "LINC00999", "DGCR9", "RCAN3AS", "CACNG6", "LINC01550", "TDRD9", "CARD17", "GBP6", "LY6E", "RSAD2", "LOC100506258", "PRKAR2A", "LTF", "IGLL5", "FAM157A", "LOC101927018", "CCR7", "FLJ42351", "IFIT3", "GPRIN1", "ANXA9", "TMEM119", "PARGP1", "A3GALT2", "CYP4F22", "PLBD1-AS1", "SELM", "NCR3", "PNPLA1", "BMX", "LOC440461", "GAMT", "CEACAM8", "TP53I13", "C1orf226", "SKA3", "DHRS3", "VRK2", "BTBD19", "ETNK2", "LOC728323", "NRIR", "ADORA2A", "GPR162", "CMPK2", "USP18", "P4HA2", "TSSK4", "EP300", "CLEC4D", "LCN10", "GPR141", "SH2D2A", "GOLGA7B", "TMIGD2", "DLL4", "HP", "CXCR6", "MAL", "C12orf57", "CLIC5", "IL4", "IGFBP6", "ERG", "HERC3", "KEL", "MSX2P1", "INE1", "PRKCQ-AS1", "FHIT", "SLC1A7", "KIAA1958", "SARDH", "PFKFB3", "SNORD89", "IFI44", "DDX60", "TMEM238", "HAR1A", "EGR1", "VNN1", "TRIM9", "TAF13", "AP3S2", "TMEM56", "KLRC2", "CACNA1C-AS2", "ALX3", "FCGBP", "CD247", "ALDH1L2", "HIST1H2AC", "RTP5", "PPARG", "AMPH", "LINC00861", "CDKL5", "MYBL2", "LOC101927051", "TLR5", "TMEM121", "BMP8B", "AK5", "RBP5", "LINC01355", "PITRM1-AS1", "CMTM1", "BIRC5", "C10orf10", "TNFRSF25", "ZAP70", "LOC105373383", "DSP", "WDR86-AS1", "RPLP2", "KREMEN1", "LOC101927550", "PDZD4", "LOC100130451", "MKI67", "LOC728743", "CXCL10", "LINC01547", "LOC645513", "ATOH8", "S1PR5", "KISS1R", "MELK", "TCEA3", "SLC22A20", "RAMP1", "FOLR2", "GGT6", "CACNA1E", "FABP6", "RAP2C-AS1", "PRRG4", "FAM63B", "BLK", "P3H3", "WIPF2", "TROAP", "FAHD2CP", "CA6", "LINC00892", "LRCH3", "BUB1B", "LOC100996286", "NT5E", "PASK", "BATF2", "TTC39C-AS1", "ACTA2", "CHIT1", "LAMC1", "TNFAIP3", "ANKRD22", "C12orf42", "SPON2", "SLX4IP", "TNNC1", "ZNF771", "SPEG", "HIST1H4H", "HTR6", "TNFRSF17", "FSD1", "LINC00266-1", "CD6", "PRSS30P", "SFTPD", "COPZ2", "BPI", "CCIN", "CDK1", "ATP2C2", "GOLGA8H", "USP44", "SLC14A1", "ZDHHC14", "SNORD50A", "LTK", "KCNG2", "MAPK11", "ACOT4", "CTSW", "C1orf106", "CDKN3", "UPB1", "CAMK2N1", "RBM5-AS1", "DNLZ", "GZMM", "PLA2G7", "ZCCHC2", "PRSS41", "RPL13", "OLR1", "BCAS4", "EPB41", "TRABD2A", "PBX4", "GZMK", "PNPLA7", "B4GALT5", "ARHGEF17", "APOA1", "GPA33", "KLRG1", "GBP5", "LOC102723701", "CENPA", "LOC285696", "KCNK5", "SUSD4", "RPS28", "ESPL1", "ITGB4", "SPAG16", "CYP4F35P", "CD3D", "BMS1P20", "CBS", "ETV2", "SPATS2L", "DUSP13", "FAM19A1", "VSIG10L", "CD2", "NHSL2", "FAAH2", "FXYD7", "NTN5", "FICD", "GLDC", "LOC101927865", "LINC00944", "PTPRCAP", "GNB3", "TRIM22", "PRRT1", "NR1I3", "BTN3A1", "STX16-NPEPL1", "TMEM191A", "SERPINB2", "UST", "GALNT12", "BUB1", "GTF2I", "FOSL2", "COL13A1", "RGS9", "NCALD", "DNAH10", "CA11", "CKB", "HSPB1", "CDC45", "ATP7B", "WNT5B", "ZNF699", "PRRT4", "GALNT14", "ZNF319", "DNAH17", "LOC283710", "CPEB4", "LY6G6C", "PPAN", "WASH5P", "HIST1H3H", "IL32", "TEPP", "CNR1", "YJEFN3", "FAM159A", "FGFBP2", "FKBP5", "BIN1", "VPREB3", "E2F8", "HK3", "CDH2", "HFE", "BEAN1-AS1", "KIFC3", "HELB", "HLA-DPB1", "GZMH", "LMNB1", "CC2D2A", "IQCH-AS1", "CTSF", "TMEM132D", "LEF1", "REL", "CBR3", "WNT10B", "LOC100289473", "APBA2", "KIF23", "LOC646471", "PNMA6A", "EPC2", "HBEGF", "S100B", "C14orf132", "KCND1", "SIPA1L2", "NCAPG", "OAS2", "C3AR1", "CSF1", "CHCHD6", "AUTS2", "IL24", "CDC42EP2", "LTBP3", "PXT1", "ADAMTS10", "BLZF1", "TPST1", "ID3", "GYG1", "EFCAB2", "MYO10", "TLE2", "SLC30A1", "CCNI", "WNT7A", "RBL2", "ERICH6-AS1", "IGFBP3", "DTL", "SORBS3", "RPS19", "SMA4", "RPS27", "BEAN1", "MIRLET7BHG", "ABCA1", "ZKSCAN7", "GPR34", "GPR153", "TRIM52", "GRASP", "RNASE2", "CENPF", "ACTG1P20", "MMP9", "ZFP82", "RTN1", "RPS21", "PCBP1-AS1", "PVRL3", "RPS10", "FER1L4", "SEPT1", "DEPDC1B", "LINC00926", "SEC14L2", "LOC100420587", "PCSK4", "HPS3", "KIR3DL1", "SPRN", "HRH2", "FAHD2B", "P3H4", "TMEM160", "ENGASE", "RAD51AP1", "ZNF233", "RAP1GAP", "TTBK2", "PINK1", "SH2D1B", "BAIAP2L1", "BECN1", "RPL13A", "INHBB", "HPCAL4", "TPM2", "ACVR2B", "CEP126", "RPL18", "B3GNT7", "ROBO3", "MYOM2", "SLC51A", "CCL5", "PROSER3", "RPL18A", "SERINC4", "ATP11B", "SMARCD3", "STMN3", "FAM173A", "CDK5RAP2", "GGH", "EIF4G3", "C18orf32", "SHCBP1", "CCM2L", "LAT", "PRF1", "ARL15", "WHAMMP1", "NBN", "ARHGEF11", "ZWINT", "BTBD11", "MCOLN2", "EYS", "GRAP", "TAP1", "FAM157C", "ANXA3", "MCEMP1", "TCF7", "IGSF9B", "SDSL", "LNPEP", "NSUN5P1", "FAM110B", "SPNS3", "ACTL10", "GCSAM", "LOC101927482", "UGCG", "CD24", "SCO2", "GMCL1", "SAMD12", "NUSAP1", "WNT1", "NME3", "LOC728084", "RGS18", "PNMA3", "KLRF1", "ENO2", "SUPT3H", "ATP5D", "ZC3H13", "PSTPIP2", "SNORD81", "SESN2", "TK1", "LHFPL2", "SCARNA10", "SERPING1", "SYT2", "SNORA33", "JAK2", "CYB5D1", "SH3RF3", "ARL14EP", "UBASH3A", "ARL5B", "SPRED2", "NELL2", "PPP2R2B", "CDH12", "WDR54", "KLF4", "MTUS1", "ELF1", "CARNS1", "CLEC10A", "CD3E", "TTK", "KBTBD7", "TPX2", "ZNF69", "AICDA", "TAMM41", "LOC100288152", "AK1", "MMP24", "SAMD9L", "RAD51", "ACE", "COLGALT2", "NEDD4", "SPEF2", "CENPE", "CRISPLD2", "TSEN54", "RCVRN", "FRMD3", "SKAP1", "CYP4F3", "TMEM161B-AS1", "KANSL1L", "SLC29A2", "CEP97", "XAF1", "CDKN1C", "PRDM5", "HBP1", "CACNG8", "RP2", "OASL", "NPIPA1", "ZNF354A", "RPL32", "GLTSCR2", "CD52", "SBK1", "ZNF703", "GBP1", "C4orf29", "ERV3-1", "ABCC2", "EPB41L5", "FFAR3", "PLEKHB1", "LOC100507387", "RPL36", "PRC1", "C19orf60", "PRSS23", "CD160", "HOPX", "SAMD10", "RPSAP9", "CDCA2", "SWT1", "NPEPL1", "RORC", "MS4A3", "BCL2L15", "CXCR5", "EPHX2", "B3GNT9", "CDCA7L", "LMTK2", "UBAP1L", "CD79B", "OBSCN", "TMEM102", "ZNF540", "SPP1", "HABP4", "LOC100130872", "APBB1", "GPC2", "CD1C", "LONRF3", "NR2C2", "NSUN7", "SIGIRR", "SNORD38B", "FAM65C", "HELZ2", "ARL4D", "ENO3", "RPL9", "IFT81", "LSMEM2", "SCARNA2", "CD5", "AHNAK2", "RPL27A", "SOX8", "TMEM161A", "ZNF81", "PXK", "LOC441081", "RPS26", "CCNB1", "PLXDC1", "NFKBIA", "PKMYT1", "SLC27A5", "LRRC4", "TARP", "MAP2K6", "EIF1B-AS1", "SCARNA17", "NUDT8", "EAF1", "TBC1D8", "SESTD1", "CLEC12B", "ZNF776", "PDCD1", "FBXL16", "SLFN14", "ATP8B3", "ZNF396", "STOM", "CCDC30", "FAM86FP", "FAM129C", "TMEM42", "ZNF607", "IL11RA", "ECHDC2", "CCNE2", "PUS7L", "EFCAB12", "TGFA", "MAPK14", "NSG1", "C9orf142", "KLHL15", "LOC102723766", "VEGFB", "TJP3", "YAE1D1", "PROS1", "CSGALNACT2", "HLA-DOA", "TUBBP5", "RPL37A", "FAM195A", "LTB4R2", "NCAPH", "EVL", "CR1", "LTC4S", "ANKS3", "RPL35", "GLIS2", "TRAPPC6A", "LIPN", "RPS12", "RPL38", "LIME1", "PHGDH", "C4orf48", "SLED1", "BACH2", "EVA1C", "GATA3", "SLC25A38", "USP32", "RPL39", "EGR2", "USP35", "ZNF662", "PLEKHG1", "CRTAM", "AANAT", "OPLAH", "FBXO6", "LOXL1", "LOC101928786", "FRMD4B", "IL1R1", "ADM2", "DNASE1L1", "STAT4", "RPLP1", "TTC9", "BEND7", "F8", "FOSB", "SLAMF6", "STK17B", "PVT1", "ARVCF", "APOBEC3C", "CDK14", "RPS14", "POLQ", "CDC42BPG", "CCDC85B", "UBR5-AS1", "TNNT3", "TCL1A", "IL7R", "PTX3", "KLHL14", "MTRNR2L1", "ZBTB46", "RPL34", "VPS9D1-AS1", "KLF7", "RPL10A", "SLC2A11", "ACTN4", "RPSA", "SGK223", "PLSCR1", "SBF2", "LINC01420", "CDCA5", "F5", "ADGRB2", "RPL23A", "MAN1A2", "LLGL2", "GINS2", "NUDT14", "TG", "RSRC1", "TMEM256", "SYTL2", "TYSND1", "TPGS1", "ABHD14B", "LSM7", "ZNF281", "CEMP1", "M1AP", "CD82", "LOC101928150", "MRPL41", "IFIH1", "CCNB2", "IFITM10", "RPS3A", "TMEM8B", "CEP135", "SIRPB2", "IRS2", "SYCE1L", "SULT1B1", "ADAM9", "ZCCHC18", "DDIAS", "LINC01278", "MTHFD2", "TIGIT", "FUOM", "FOSL1", "RPS16", "BRAF", "CACNA1I", "EXT1", "GPR82", "LRRC45", "STAG1", "KIF11", "NCAM1", "RNF24", "KIAA2018", "SAMD9", "ABCA2", "PVRIG", "RASGRF2", "IRAK2", "ARHGEF4", "ZNF682", "RASSF7", "THAP7", "KIF1B", "GAS5", "KIF15", "RMRP", "GALM", "LYNX1", "C7orf50", "ATXN1L", "KCNN4", "LDLRAP1", "RETSAT", "RNF10", "NOXA1", "UBXN7", "UHRF1BP1L", "TESPA1", "MB21D1", "LINC00116", "FOXM1", "CCR6", "NMUR1", "RPL22", "LCK", "MEGF9", "CLSPN", "MIR4697HG", "PPP1R15A", "ADCK5", "SAMD3", "RPS27A", "TPPP", "PIWIL4", "ANKRD33B", "IL18RAP", "SNRPD2", "WASF1", "ALDH5A1", "FAM149B1", "RPS18", "SLCO4C1", "MGC16275", "ZNF837", "SAMHD1", "NSMF", "PDIA6", "SNX20", "ATF7IP2", "BPHL", "RPL35A", "NFKBIZ", "LMF1", "MGMT", "PPL", "FAM120C", "APRT", "RANGRF", "PHLDB3", "IDNK", "RPS15", "LGR6", "MOB1A", "CREB5", "THEM4", "ENDOG", "RPL7", "EDN1", "ZNF808", "SNHG15", "RPL3", "SMCR8", "FCMR", "ZNF449", "ZBTB37", "NHLRC4", "DHRS9", "C1QTNF3", "MS4A1", "AFF2", "FBXO32", "SLC6A6", "ZSCAN30", "CHPT1", "PARP14", "MZT2B", "FLT3LG", "TCTN2", "MAN1A1", "LOC728175", "ALOX5", "DUSP18", "ZC3H12D", "PECR", "LRRC37A3", "EPB41L4A-AS1", "SPAG5", "KDM7A", "DDX31", "NCOA6", "SLC6A12", "FCER1A", "FBXL13", "AKR7A2", "PRR5", "PRKCE", "PPP1R3D", "SYNM", "GLI1", "NTNG2", "EEF1G", "LOC643802", "MAGEE1", "PIK3AP1", "RPS8", "SNHG19", "C19orf70", "GAS7", "PHF12", "ATP11A", "SLC22A23", "ATP6V0E2", "UCKL1-AS1", "TIPARP", "BEX2", "FAM102A", "RPS20", "TTC16", "PLP2", "HAPLN3", "C19orf24", "ZNF397", "FOXO4", "TRAPPC2B", "QTRT1", "CDK10", "PDZD8", "GIPC1", "CD274", "C9orf139", "SPIB", "KLHDC7B", "ZNF32", "ELMO2", "AK4", "SAMD4B", "MFSD3", "EXD3", "SLC37A3", "RPL8", "TRIM56", "NMB", "PIPOX", "PXN-AS1", "SLC2A5")

Please Note: Over-representation services return large responses. To reduce the size of this vignette article, we will only display the first 10 rows of each Data Frame.

Enrichr

Enrichr , developed in the Ma’ayan Lab, is a service to perform enrichment analysis against a considerable number of curated gene set libraries across various species.

We have thoroughly covered performing enrichment analysis with Enrichr in the vignette article Enrichr & rbioapi. Therefore, we will only provide a brief demonstration here.

First, let us retrieve a list of available libraries in Enrichr using the rba_enrichr_libs() function. You can find the available Enrichr libraries in the libraryName column in the returned data frame.

enrichr_libs <- rba_enrichr_libs()
#> Retrieving List of available libraries and statistics from Enrichr human.

While the only required argument of rba_enrichr() function is gene_list, here we use gene_set_library argument to request an enrichment analysis only against “KEGG_2021_Human” library.

enrichr_enrich <- rba_enrichr(gene_list = covid_critical,
                              gene_set_library = "KEGG_2021_Human")
#> --Step 1/3:
#> Retrieving List of available libraries and statistics from Enrichr human.
#> --Step 2/3:
#> Uploading 1214 gene symbols to Enrichr human.
#> --Step 3/3:
#> Performing enrichment analysis on gene-list 51823925 against Enrichr human library: KEGG_2021_Human.

In the gene_set_library parameter, you can also provide multiple gene set libraries or leave it as the default parameter NULL to perform the enrichment analysis using every gene set library available in Enrichr. Also note that when regex_library_name parameter is TRUE (the default value), gene_set_library parameter will be considered as a regex pattern, so partial matchs will be also selected.

enrichr_enrich_kegg <- rba_enrichr(gene_list = covid_critical,
                                   gene_set_library = "kegg",
                                   regex_library_name = TRUE # default value
)
#> --Step 1/3:
#> Retrieving List of available libraries and statistics from Enrichr human.
#> --Step 2/3:
#> Uploading 1214 gene symbols to Enrichr human.
#> --Step 3/3:
#> Performing enrichment analysis on gene-list 51823926 using multiple Enrichr human libraries.
#> Note: You have selected '6' Enrichr human libraries. Note that for each library, a separate call should be sent to Enrichr server. Thus, this could take a while depending on the number of selected libraries and your network connection.

As you can see below, when than one library is selected, the results will be a list where each of its elements is the enrichment results of one library:

str(enrichr_enrich_kegg, max.level = 2)
#> List of 6
#>  $ KEGG_2013      :'data.frame': 139 obs. of  9 variables:
#>   ..$ Term                : chr [1:139] "HSA03010 RIBOSOME" "HSA04640 HEMATOPOIETIC CELL LINEAGE" "HSA00260 GLYCINE SERINE AND THREONINE METABOLISM" "HSA04110 CELL CYCLE" ...
#>   ..$ Overlap             : chr [1:139] "30/98" "16/88" "8/45" "13/115" ...
#>   ..$ P.value             : num [1:139] 5.76e-14 7.05e-05 5.20e-03 2.22e-02 2.40e-02 ...
#>   ..$ Adjusted.P.value    : num [1:139] 8.01e-12 4.90e-03 2.41e-01 6.67e-01 6.67e-01 ...
#>   ..$ Old.P.value         : int [1:139] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Old.Adjusted.P.value: int [1:139] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Odds.Ratio          : num [1:139] 6.97 3.47 3.36 1.98 1.61 ...
#>   ..$ Combined.Score      : num [1:139] 212.62 33.19 17.68 7.55 5.99 ...
#>   ..$ Genes               : chr [1:139] "RPL3;RPL32;RPL34;RPL8;RPL10A;RPL9;RPL7;RPS16;RPL18A;RPS18;RPL35;RPL13;RPL38;RPL18;RPL39;RPS10;RPS12;RPS8;RPL13A"| __truncated__ "CR1;CSF1;IL1R1;IL11RA;ITGA1;FLT3LG;CD3E;CD1C;CD3D;CD2;IL4;CD5;CD38;CD24;IL7R;MS4A1" "GAMT;ALAS2;GLDC;MAOA;CBS;SARDH;PIPOX;PHGDH" "CDKN1C;BUB1B;CDC6;PKMYT1;CDC25A;CDC20;RBL2;CCNB2;CCNB1;CCNE2;ESPL1;EP300;BUB1" ...
#>  $ KEGG_2015      :'data.frame': 131 obs. of  9 variables:
#>   ..$ Term                : chr [1:131] "ribosome" "hematopoietic cell lineage" "glycine serine and threonine metabolism" "cell cycle" ...
#>   ..$ Overlap             : chr [1:131] "30/71" "16/88" "8/44" "13/104" ...
#>   ..$ P.value             : num [1:131] 1.90e-18 7.05e-05 4.51e-03 1.02e-02 1.56e-02 ...
#>   ..$ Adjusted.P.value    : num [1:131] 2.48e-16 4.62e-03 1.97e-01 3.34e-01 4.10e-01 ...
#>   ..$ Old.P.value         : int [1:131] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Old.Adjusted.P.value: int [1:131] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Odds.Ratio          : num [1:131] 11.58 3.47 3.45 2.22 1.68 ...
#>   ..$ Combined.Score      : num [1:131] 472.72 33.19 18.66 10.2 6.98 ...
#>   ..$ Genes               : chr [1:131] "RPL3;RPL32;RPL34;RPL10A;RPL8;RPL9;RPL7;RPS16;RPL18A;RPS18;RPL35;RPL13;RPL38;RPL18;RPL39;RPS10;RPS12;RPS8;RPL13A"| __truncated__ "CR1;CSF1;IL1R1;IL11RA;ITGA1;FLT3LG;CD3E;CD1C;CD3D;CD2;IL4;CD5;CD38;CD24;IL7R;MS4A1" "GAMT;ALAS2;GLDC;MAOA;CBS;SARDH;PIPOX;PHGDH" "CDKN1C;BUB1B;CDC6;PKMYT1;CDC25A;CDC20;RBL2;CCNB2;CCNB1;CCNE2;ESPL1;EP300;BUB1" ...
#>  $ KEGG_2016      :'data.frame': 260 obs. of  9 variables:
#>   ..$ Term                : chr [1:260] "Ribosome Homo sapiens hsa03010" "Hematopoietic cell lineage Homo sapiens hsa04640" "Glycine, serine and threonine metabolism Homo sapiens hsa00260" "Cell cycle Homo sapiens hsa04110" ...
#>   ..$ Overlap             : chr [1:260] "38/137" "16/88" "9/40" "17/124" ...
#>   ..$ P.value             : num [1:260] 9.80e-16 7.05e-05 5.36e-04 1.34e-03 6.17e-03 ...
#>   ..$ Adjusted.P.value    : num [1:260] 2.55e-13 9.16e-03 4.65e-02 8.72e-02 3.21e-01 ...
#>   ..$ Old.P.value         : int [1:260] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Old.Adjusted.P.value: int [1:260] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Odds.Ratio          : num [1:260] 6.1 3.47 4.52 2.48 3.63 ...
#>   ..$ Combined.Score      : num [1:260] 210.8 33.2 34 16.4 18.4 ...
#>   ..$ Genes               : chr [1:260] "RPL3;RPL32;RPL34;RPLP1;RPL8;RPL10A;RPL9;RPL7;RPS15;RPS14;RPS16;RPS19;RPL18A;RPS18;RPL36;RPL35;RPLP2;RPL13;RPL38"| __truncated__ "CR1;CSF1;IL1R1;IL11RA;ITGA1;FLT3LG;CD3E;CD1C;CD3D;CD2;IL4;CD5;CD38;CD24;IL7R;MS4A1" "GAMT;ALAS2;GLDC;MAOA;CBS;SARDH;PIPOX;PHGDH;SDSL" "CDKN1C;BUB1B;TTK;CDC6;PKMYT1;CDC25A;CDC20;RBL2;CCNB2;CCNB1;STAG1;CDC45;CCNE2;ESPL1;CDK1;EP300;BUB1" ...
#>  $ KEGG_2019_Human:'data.frame': 272 obs. of  9 variables:
#>   ..$ Term                : chr [1:272] "Ribosome" "Hematopoietic cell lineage" "Th1 and Th2 cell differentiation" "Th17 cell differentiation" ...
#>   ..$ Overlap             : chr [1:272] "38/153" "18/97" "16/92" "17/107" ...
#>   ..$ P.value             : num [1:272] 4.86e-14 1.91e-05 1.22e-04 2.38e-04 5.36e-04 ...
#>   ..$ Adjusted.P.value    : num [1:272] 1.32e-11 2.59e-03 1.11e-02 1.62e-02 2.92e-02 ...
#>   ..$ Old.P.value         : int [1:272] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Old.Adjusted.P.value: int [1:272] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Odds.Ratio          : num [1:272] 5.25 3.56 3.29 2.95 4.52 ...
#>   ..$ Combined.Score      : num [1:272] 160.8 38.7 29.6 24.6 34 ...
#>   ..$ Genes               : chr [1:272] "RPL3;RPL32;RPL34;RPLP1;RPL8;RPL10A;RPL9;RPL7;RPS15;RPS14;RPS16;RPS19;RPL18A;RPS18;RPL36;RPL35;RPLP2;RPL13;RPL38"| __truncated__ "CR1;CSF1;IL1R1;IL11RA;ITGA1;FLT3LG;CD1C;CD3E;CD3D;IL4;CD2;CD5;HLA-DPB1;CD38;HLA-DOA;IL7R;CD24;MS4A1" "GATA3;CD3E;MAPK14;CD3D;DLL4;IL4;NFKBIA;ZAP70;MAPK11;LCK;STAT4;HLA-DPB1;CD247;HLA-DOA;JAK2;LAT" "IL1R1;RORC;GATA3;CD3E;MAPK14;HIF1A;CD3D;IL4;NFKBIA;ZAP70;MAPK11;LCK;HLA-DPB1;CD247;HLA-DOA;JAK2;LAT" ...
#>  $ KEGG_2019_Mouse:'data.frame': 267 obs. of  9 variables:
#>   ..$ Term                : chr [1:267] "Ribosome" "Glycine, serine and threonine metabolism" "Th1 and Th2 cell differentiation" "Cell cycle" ...
#>   ..$ Overlap             : chr [1:267] "37/170" "9/40" "14/87" "17/123" ...
#>   ..$ P.value             : num [1:267] 7.70e-12 5.36e-04 7.19e-04 1.23e-03 1.23e-03 ...
#>   ..$ Adjusted.P.value    : num [1:267] 2.06e-09 6.40e-02 6.40e-02 6.58e-02 6.58e-02 ...
#>   ..$ Old.P.value         : int [1:267] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Old.Adjusted.P.value: int [1:267] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Odds.Ratio          : num [1:267] 4.41 4.52 2.99 2.5 2.69 ...
#>   ..$ Combined.Score      : num [1:267] 112.8 34 21.6 16.8 18 ...
#>   ..$ Genes               : chr [1:267] "RPL3;RPL32;RPL34;RPLP1;RPL8;RPL10A;RPL9;RPL7;RPS15;RPS14;RPS16;RPS19;RPL18A;RPS18;RPL36;RPL35;RPLP2;RPL13;RPL38"| __truncated__ "GAMT;ALAS2;GLDC;MAOA;CBS;SARDH;PIPOX;PHGDH;SDSL" "GATA3;CD3E;MAPK14;CD3D;DLL4;IL4;NFKBIA;ZAP70;MAPK11;LCK;STAT4;CD247;JAK2;LAT" "CDKN1C;BUB1B;TTK;CDC6;PKMYT1;CDC25A;CDC20;RBL2;CCNB2;CCNB1;STAG1;CDC45;CCNE2;ESPL1;CDK1;EP300;BUB1" ...
#>  $ KEGG_2021_Human:'data.frame': 285 obs. of  9 variables:
#>   ..$ Term                : chr [1:285] "Coronavirus disease" "Ribosome" "Hematopoietic cell lineage" "Th1 and Th2 cell differentiation" ...
#>   ..$ Overlap             : chr [1:285] "51/232" "38/158" "18/99" "16/92" ...
#>   ..$ P.value             : num [1:285] 5.46e-16 1.46e-13 2.54e-05 1.22e-04 2.38e-04 ...
#>   ..$ Adjusted.P.value    : num [1:285] 1.56e-13 2.08e-11 2.42e-03 8.70e-03 1.34e-02 ...
#>   ..$ Old.P.value         : int [1:285] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Old.Adjusted.P.value: int [1:285] 0 0 0 0 0 0 0 0 0 0 ...
#>   ..$ Odds.Ratio          : num [1:285] 4.51 5.03 3.48 3.29 2.95 ...
#>   ..$ Combined.Score      : num [1:285] 158.4 148.5 36.8 29.6 24.6 ...
#>   ..$ Genes               : chr [1:285] "RPL3;RPL32;RPL34;RPLP1;RPL8;RPL10A;RPL9;RPL7;RPS15;IFIH1;RPS14;RPS16;RPL18A;RPS19;RPS18;C3AR1;RPL36;RPL35;RPLP2"| __truncated__ "RPL3;RPL32;RPL34;RPLP1;RPL10A;RPL8;RPL9;RPL7;RPS15;RPS14;RPS16;RPL18A;RPS19;RPS18;RPL36;RPL35;RPLP2;RPL13;RPL38"| __truncated__ "CR1;CSF1;IL11RA;IL1R1;ITGA1;FLT3LG;CD1C;CD3E;CD3D;IL4;CD2;CD5;HLA-DPB1;CD38;HLA-DOA;CD24;IL7R;MS4A1" "GATA3;CD3E;MAPK14;CD3D;DLL4;IL4;NFKBIA;ZAP70;MAPK11;LCK;STAT4;HLA-DPB1;CD247;HLA-DOA;JAK2;LAT" ...

Reactome Analysis services

Reactome curates extensive and top-quality cellular pathways data across various species. Given the fact that proteins operate in organized units, which are the pathways, analyzing your list against Reactome pathways gene sets can provide valuable insights into the functional trajectories of your results.

You can refer to Reactome & rbioapi article for in-depth coverage of Reactome services. Here we will only demonstrate the over-representation functionality

The function rba_reactome_analysis() accepts gene lists or a table as input. If a table is supplied, the first column should consist of identifiers, and the next column should be numerical gene expression values. Refer to rba_reactome_analysis() function’s manual for details. Here, we will use our gene list of critical COVID:

reactome <- rba_reactome_analysis(input = covid_critical)
#> Retrieving Reactome Analysis Results of your supplied Identifiers.

Let us examine the response’s structure:

str(reactome, max.level = 1)
#> List of 8
#>  $ summary            :List of 7
#>  $ expression         :List of 1
#>  $ identifiersNotFound: int 563
#>  $ pathwaysFound      : int 9953
#>  $ pathways           :'data.frame': 9953 obs. of  19 variables:
#>  $ resourceSummary    :'data.frame': 4 obs. of  3 variables:
#>  $ speciesSummary     :'data.frame': 15 obs. of  5 variables:
#>  $ warnings           : chr "Missing header. Using a default one."

The list’s element names are self-explanatory. The results are returned as a data frame in the pathways element. Note that Reactome will map your provided gene identifiers and some identifiers may not be found. Hence, keep an eye on the identifiersNotFound element.

Naturally, you can alter the analysis parameters using the rba_reactome_analysis() function’s arguments. The used parameters are returned in the “summary” element.

str(reactome$summary)
#> List of 7
#>  $ token         : chr "MjAyMjA2MTYyMTUzNDBfMTAw"
#>  $ projection    : logi FALSE
#>  $ interactors   : logi FALSE
#>  $ type          : chr "OVERREPRESENTATION"
#>  $ sampleName    : chr ""
#>  $ text          : logi TRUE
#>  $ includeDisease: logi TRUE

In the above code chunk, note the “token” associated with your analysis. You can use this token to perform the following.

Retrieve the analysis results:

reactome_2 <- rba_reactome_analysis_token(reactome$summary$token)

In addition to the main response, you can download other data associated with your request. see the argument request in the rba_reactome_analysis_download() function’s manual for more information. Here, we will download a CSV file with the input identifiers which has not been found:

rba_reactome_analysis_download(token = reactome$summary$token,
                               request = "not_found_ids",
                               save_to = "my_analysis.csv")

Reactome also generates a nice report of your analysis in pdf format, make sure to try it:

rba_reactome_analysis_pdf(token = reactome$summary$token,
                          species = 9606, #Homo sapiens
                          save_to = "my_analysis.pdf")

PANTHER

PANTHER (protein analysis through evolutionary relationships) is a project that provides classification systems of genes and proteins. PANTHER also provides an enrichment service. In fact, the enrichment tool available on the Gene Ontology (GO) website is powered by PANTHER. Just like the previous sections, we will only review the enrichment functionality here; for an in-depth review of PANTHER refer to the vignette article “PANTHER & rbioapi”.

To perform the analysis, first, we need to choose the classifications (i.e., gene sets) that we want to compare our results against it. To retrieve a list of available gene sets, we call the following function:

panther_sets <- rba_panther_info(what = "datasets")
#> Retrieving available annotation datasets.

The Gene Ontology (GO) project is one of the pinnacles of scientists’ collective effort in bioinformatics. The GO Consortium provides a comprehensive model of biological systems. In short, GO curates a thoroughly designed directed acyclic graph (DAG) of ontologies. You may think of it as a tree of terms, where as it branches out, the terms become more specific). Each protein may be annotated with one or more terms. The terms are organized in three domains: “Molecular Function,” “Biological Process,” and “Cellular Component”. GO slim datasets refer to subsets which are a cut-down version of GO terms. If you are not familiar with GO, I strongly encourage you to see this page and follow the links it provides: About the GO resource.

Here, we demonstrate the enrichment analysis using the Biological Process domain:

panther_enrich <- rba_panther_enrich(genes = covid_critical,
                                     organism = 9606, #Homo sapiens
                                     annot_dataset = "GO:0008150" #Biological Process
                                     )
#> Performing over-representation enrichment analysis of 1214 input genes of organism 9606 against GO:0008150 datasets.

In addition to the enrichment results, PANTHER returns other useful information about your analysis. The names are self-explanatory:

str(panther_enrich)
#> List of 9
#>  $ reference                 :List of 3
#>   ..$ organism      : chr "Homo sapiens"
#>   ..$ mapped_count  : int 20589
#>   ..$ unmapped_count: int 0
#>  $ input_list                :List of 5
#>   ..$ organism      : chr "Homo sapiens"
#>   ..$ mapped_count  : int 1048
#>   ..$ mapped_id     : chr [1:1048] "SCT" "C4BPA" "FAM159A" "TSEN54" ...
#>   ..$ unmapped_count: int 166
#>   ..$ unmapped_id   : chr [1:166] "LOC200772" "ELK2AP" "CLRN1-AS1" "LOC100272216" ...
#>  $ result                    :'data.frame':  15672 obs. of  9 variables:
#>   ..$ number_in_list     : int [1:15672] 38 131 1 130 2 131 187 134 123 123 ...
#>   ..$ fold_enrichment    : num [1:15672] 5.8748 1.7308 0.0433 1.7023 0.0816 ...
#>   ..$ fdr                : num [1:15672] 9.12e-12 4.47e-05 3.19e-05 5.37e-05 6.13e-05 ...
#>   ..$ expected           : num [1:15672] 6.47 75.69 23.11 76.37 24.52 ...
#>   ..$ number_in_reference: int [1:15672] 124 1451 443 1464 470 1500 2375 1558 1400 1403 ...
#>   ..$ pValue             : num [1:15672] 5.82e-16 5.70e-09 6.11e-09 1.37e-08 1.96e-08 ...
#>   ..$ plus_minus         : chr [1:15672] "+" "+" "-" "+" ...
#>   ..$ term.id            : chr [1:15672] "GO:0002181" "GO:0006952" "GO:0050911" "GO:0009607" ...
#>   ..$ term.label         : chr [1:15672] "cytoplasmic translation" "defense response" "detection of chemical stimulus involved in sensory perception of smell" "response to biotic stimulus" ...
#>  $ search                    :List of 1
#>   ..$ search_type: chr "overrepresentation"
#>  $ tool_release_date         : int 20220712
#>  $ enrichment_test_type      : chr "FISHER"
#>  $ annotDataSet              : chr "GO:0008150"
#>  $ annot_version_release_date: chr "GO Ontology database DOI:  10.5281/zenodo.6399963 Released 2022-03-22"
#>  $ correction                : chr "FDR"

The enrichment results are returned as a Data Frame with an element named result:

STRING

In addition to proteins interaction data, STRING also curates proteins/genes annotations and provides enrichment analysis services. You can perform the analysis against multiple gene sets. Some of the gene sets can be accessed using other services but two gene sets are exclusive to STRING. See “D. Szklarczyk et al.: Nucleic Acids Res. 49, D1 (2021)” for further information. Again. We will only review the enrichment functionality here; for an in-depth review of STRING refer to the vignette article “STRING & rbioapi”.

We can directly supply our covid_critical variable to STRING’s enrichment function:

string_enrich <- rba_string_enrichment(ids = covid_critical,
                                       species = 9606 #Homo sapiens
                                       )
#> Performing functional enrichment of 1214 Input Identifiers.

As you can see, by default a list is returned where each element is a data Frame with enrichment analysis results against one of the gene sets listed above:

str(string_enrich, max.level = 1)
#> List of 13
#>  $ COMPARTMENTS     :'data.frame':   18 obs. of  10 variables:
#>  $ Component        :'data.frame':   19 obs. of  10 variables:
#>  $ DISEASES         :'data.frame':   8 obs. of  10 variables:
#>  $ Function         :'data.frame':   2 obs. of  10 variables:
#>  $ HPO              :'data.frame':   3 obs. of  10 variables:
#>  $ KEGG             :'data.frame':   6 obs. of  10 variables:
#>  $ Keyword          :'data.frame':   16 obs. of  10 variables:
#>  $ NetworkNeighborAL:'data.frame':   36 obs. of  10 variables:
#>  $ PMID             :'data.frame':   100 obs. of  10 variables:
#>  $ Process          :'data.frame':   119 obs. of  10 variables:
#>  $ RCTM             :'data.frame':   37 obs. of  10 variables:
#>  $ TISSUES          :'data.frame':   18 obs. of  10 variables:
#>  $ WikiPathways     :'data.frame':   1 obs. of  10 variables:

In the “PMID” element, we see which PubMed papers are over-represented with our gene list. To keep the table tidy, we are not displaying the inputGenes and preferredNames columns.

You can retrieve the annotations associated with your input protein(s). The difference here is that no statistical analysis will be performed and the full annotations of your input gene will be returned. Because of potentially unwieldy response, allow_pubmed is set fo FALSE by default. Here we set it to TRUE to also retrieve the PubMed paper abstracts that include our gene.

string_annot <- rba_string_annotations(ids = "CD177",
                                       species = 9606, #Homo sapiens
                                       allow_pubmed = TRUE
                                       )
#> Retrieving functional annotations of 1 Input Identifiers.

As you can see, Our input gene has a large number of annotations. Let us take a look at the PMID Data Frame, which contains the PubMed Abstract paper terms:

str(string_annot, max.level = 1)
#> List of 13
#>  $ COMPARTMENTS     :'data.frame':   31 obs. of  8 variables:
#>  $ Component        :'data.frame':   43 obs. of  8 variables:
#>  $ DISEASES         :'data.frame':   6 obs. of  8 variables:
#>  $ Function         :'data.frame':   9 obs. of  8 variables:
#>  $ HPO              :'data.frame':   8 obs. of  8 variables:
#>  $ InterPro         :'data.frame':   1 obs. of  8 variables:
#>  $ Keyword          :'data.frame':   14 obs. of  8 variables:
#>  $ NetworkNeighborAL:'data.frame':   2 obs. of  8 variables:
#>  $ Pfam             :'data.frame':   1 obs. of  8 variables:
#>  $ PMID             :'data.frame':   1775 obs. of  8 variables:
#>  $ Process          :'data.frame':   130 obs. of  8 variables:
#>  $ RCTM             :'data.frame':   7 obs. of  8 variables:
#>  $ TISSUES          :'data.frame':   12 obs. of  8 variables:

miEAA

The miRNA Enrichment Analysis and Annotation Tool (miEAA) is a service provided by the Chair for Clinical Bioinformatics at Saarland University. What makes miEAA unique among other services presented here is that miEAA curates miRNA sets. Hence you can directly perform miRNA enrichment analysis across various species with the services miEAA provides.

We have in-depth covered performing enrichment analysis with miEAA in the vignette article “miEEA & rbioapii”. Therefore, we will only provide a brief demonstration here.

We can not use our covid_critical gene list here. We rather use a miRNA list from the paper “A. Parray et al.: Vaccines 9, 1056 (2021)”. They used microarray to find differentially expressed miRNA in the severe versus mild COVID 19 patients’ blood.

covid_mirna <- c("hsa-miR-3609", "hsa-miR-199a-5p", "hsa-miR-139-5p", "hsa-miR-145-5p","hsa-miR-3651", "hsa-miR-1273h-3p", "hsa-miR-4632-5p","hsa-miR-6861-5p", "hsa-miR-6802-5p","hsa-miR-5196-5p","hsa-miR-92b-5p", "hsa-miR-6805-5p","hsa-miR-98-5p","hsa-miR-3185", "hsa-miR-572","hsa-miR-371b-5p","hsa-miR-3180", "hsa-miR-8073","hsa-miR-4750-5p","hsa-miR-6075", "hsa-let-7i-5p","hsa-miR-1231","hsa-miR-885-3p")

While not necessary, we can first see what enrichment categories are available for our mature human miRNA:

rba_mieaa_cats(mirna_type = "mature",
               species = 9606 #Homo sapiens
)
#> Retrieving available enrichment categories of miRNA for Homo sapiens.
#> Annotations derived over miRTarBase (Gene Ontology) 
#>                    "GO_Annotations_indirect_mature" 
#>                          Annotation (Gene Ontology) 
#>                             "GO_Annotations_mature" 
#>                                     Pathways (KEGG) 
#>                                       "KEGG_mature" 
#>                             exRNA forms (miRandola) 
#>                                  "miRandola_mature" 
#>                      Chromosomal location (miRBase) 
#>                        "miRBase_Chromosomes_mature" 
#>                              Conservation (miRBase) 
#>       "miRBase_Conserved_miRNAs_5_organisms_mature" 
#>                                Confidence (miRBase) 
#>                    "miRBase_High_confidence_mature" 
#>                               Seed family (miRBase) 
#>                        "miRBase_Seed_family_mature" 
#>                   GO Biological process (miRPathDB) 
#>            "miRPathDB_GO_Biological_process_mature" 
#>                   GO Cellular component (miRPathDB) 
#>            "miRPathDB_GO_Cellular_component_mature" 
#>                   GO Molecular function (miRPathDB) 
#>            "miRPathDB_GO_Molecular_function_mature" 
#>                                    KEGG (miRPathDB) 
#>                             "miRPathDB_KEGG_mature" 
#>                                Reactome (miRPathDB) 
#>                         "miRPathDB_Reactome_mature" 
#>                            WikiPathways (miRPathDB) 
#>                     "miRPathDB_WikiPathways_mature" 
#>                           Target genes (miRTarBase) 
#>                                 "miRTarBase_mature" 
#>                                  Diseases (miRWalk) 
#>                           "miRWalk_Diseases_mature" 
#>                             Gene Ontology (miRWalk) 
#>                                 "miRWalk_GO_mature" 
#>                                    Organs (miRWalk) 
#>                             "miRWalk_Organs_mature" 
#>                                  Pathways (miRWalk) 
#>                           "miRWalk_Pathways_mature" 
#>                                     Diseases (MNDR) 
#>                                       "MNDR_mature" 
#>                              Interactions (NPInter) 
#>                                    "NPInter_mature" 
#>                                          Gender/Age 
#>                       "Published_Age_gender_mature" 
#>                          Cell-type specific (Atlas) 
#>                    "Published_cell_specific_mature" 
#>                                  Published Diseases 
#>                         "Published_Diseases_mature" 
#>                                        Immune cells 
#>                     "Published_Immune_cells_mature" 
#>                            Localization (RNALocate) 
#>                                  "RNALocate_mature" 
#>                                      Drugs (SM2miR) 
#>                                     "SM2miR_mature" 
#>                  Expressed in tissue (Tissue Atlas) 
#>                                "TissueAtlas_mature"

Next, we will use the wrapper function available to perform the necessary steps in one function call. Note that we have supplied the categories parameter. If left NULL, the enrichment will be performed against every available category.

mieaa_enrich <- rba_mieaa_enrich(test_set = covid_mirna,
                                 mirna_type = "mature",
                                 test_type = "ORA",
                                 species = 9606,
                                 categories = "miRPathDB_GO_Biological_process_mature")
#>  -- Step 1/3: Submitting Enrichment request:
#> Submitting ORA enrichment request for 23 miRNA IDs of species Homo sapiens to miEAA servers.
#> 
#>  -- Step 2/3: Checking for Submitted enrichment job's status every 5 seconds.
#>     Your submitted job ID is: 5666398f-f763-4ea0-a781-864158b8bf3b
#> .
#> 
#>  -- Step 3/3: Retrieving the results of the finished enrichment job.
#> Retrieving results of submitted enrichment request with ID: 5666398f-f763-4ea0-a781-864158b8bf3b

How to cite?

rbioapi is an interface between you and other databases and services. Thus, if you have used rbioapi in published research, kindly in addition to citing rbioapi, make sure to fully and properly cite the databases/services you have used. Suggested citations have been added in the functions’ manuals, under the “references” section; Nevertheless, it is the user’s responsibility to check for proper citations and to properly cite the database/services that they have used.

Please see the rbioapi’s main vignette article for more details.


Session info

#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] rbioapi_0.7.7
#> 
#> loaded via a namespace (and not attached):
#>  [1] knitr_1.39        magrittr_2.0.3    R6_2.5.1          ragg_1.2.2       
#>  [5] rlang_1.0.4       fastmap_1.1.0     httr_1.4.3        stringr_1.4.0    
#>  [9] tools_4.2.1       DT_0.23           xfun_0.31         cli_3.3.0        
#> [13] jquerylib_0.1.4   crosstalk_1.2.0   htmltools_0.5.3   systemfonts_1.0.4
#> [17] yaml_2.3.5        digest_0.6.29     rprojroot_2.0.3   pkgdown_2.0.6    
#> [21] textshaping_0.3.6 purrr_0.3.4       htmlwidgets_1.5.4 sass_0.4.2       
#> [25] fs_1.5.2          curl_4.3.2        memoise_2.0.1     cachem_1.0.6     
#> [29] evaluate_0.15     rmarkdown_2.14    stringi_1.7.8     compiler_4.2.1   
#> [33] bslib_0.4.0       desc_1.4.1        jsonlite_1.8.0