MSigDB is a widely used gene set database in bio-research. However, navigating and querying pathways of interest on its website can be challenging. This R package is designed to facilitate more convenient and efficient querying of pathways either based on specific genes or using regular expression patterns to match pathway names.
You can install the development version of r4msigdb like so:
devtools::install('snowGlint/r4msigdb')To retrieve pathways related to a specific topic (e.g., cell programmed death):
library(r4msigdb)
query(species = 'Hs', pathway = 'OPTOSIS') |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <list>
#> 1: C2:CGP ALCALA_APOPTOSIS HCCS,MATK,FAS,CYFIP2,ELOVL1,PFKP,...
#> 2: C2:CGP BROCKE_APOPTOSIS_REVERSED_BY_IL6 DPM1,RALA,PHTF2,ADIPOR2,CD44,SH2D2A,...
#> 3: C2:CGP CONCANNON_APOPTOSIS_BY_EPOXOMICIN_DN ICA1,ETV1,TRAPPC6A,DNASE1L1,TMSB10,HDAC9,...
#> 4: C2:CGP CONCANNON_APOPTOSIS_BY_EPOXOMICIN_UP TAC1,IFRD1,TSPAN9,GCLM,FAS,CD44,...
#> 5: C2:CGP DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_DN LASP1,BLTP2,METTL13,CD9,NISCH,BRCA1,...
#> 6: C2:CGP DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_UP AK2,CDC27,ACSM3,ZFX,TAC1,IFRD1,...This will return a list of pathways associated with OPTOSIS and the
genes involved in each pathway.
If you want to find pathways related to specific genes (e.g., PTPRC and TP53):
query(species = 'Hs', symbols = c('PTPRC', 'TP53')) |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <list>
#> 1: C1 chr17p13 CAMKK1,DVL2,DHX33,PAFAH1B1,GAS7,RABEP1,...
#> 2: C1 chr1q31 GLRX2,TPR,PTGS2,RGS2,RO60,UCHL5,...
#> 3: C2:CGP ABRAHAM_ALPC_VS_MULTIPLE_MYELOMA_DN TCF3,XRCC5,PSEN1,CCNB1,IL6ST,CDK4,...
#> 4: C2:CGP ACOSTA_PROLIFERATION_INDEPENDENT_MYC_TARGETS_DN LASP1,ACSM3,GDE1,CAPN1,CHPF2,KCNH2,...
#> 5: C2:CGP BENITEZ_GBM_PROTEASOME_INHIBITION_RESPONSE DVL2,MXD1,ASNS,MLF2,GPATCH2L,FBXO7,...
#> 6: C2:CGP BENPORATH_MYC_MAX_TARGETS DPM1,GCLC,M6PR,RECQL,GCFC2,PDK2,...This will provide pathways where the specified genes are involved.
Advanced users familiar with data.table can perform custom queries. For
example, to retrieve all pathways collection_name == 'H':
query(species = 'Hs')[.('H')] |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <list>
#> 1: H HALLMARK_ADIPOGENESIS AK2,NDUFAB1,ADIPOR2,UQCRC1,PHLDB1,RETSAT,...
#> 2: H HALLMARK_ALLOGRAFT_REJECTION STAB1,BRCA1,WAS,FAS,CAPG,HDAC9,...
#> 3: H HALLMARK_ANDROGEN_RESPONSE PGM3,PIAS1,RRP12,APPBP2,GNAI3,IDI1,...
#> 4: H HALLMARK_ANGIOGENESIS NRP1,JAG1,TIMP1,VTN,VEGFA,ITGAV,...
#> 5: H HALLMARK_APICAL_JUNCTION CD99,SKAP2,ITGA3,PKD1,CLDN11,VCL,...
#> 6: H HALLMARK_APICAL_SURFACE ADIPOR2,BRCA1,DCBLD2,CROCC,IL2RB,ATP6V0A4,...You can also use .unlist = TRUE to unlist the symbols column in any of
the above query methods.
query(species = 'Hs', .unlist = TRUE) |> head()
#> Key: <collection_name, standard_name>
#> collection_name standard_name symbol
#> <char> <char> <char>
#> 1: C1 MT MT-CO2
#> 2: C1 MT MT-ND2
#> 3: C1 MT MT-CO1
#> 4: C1 MT MT-ND3
#> 5: C1 MT MT-ND4
#> 6: C1 MT MT-ATP6library(fgsea)
library(data.table)
data(exampleRanks)
pathway <- query(species = 'Hs', pathway = 'OPTOSIS')
pathway <- pathway[, .(standard_name, symbol)]
# substitute names with random symbols
set.seed(2024)
names(exampleRanks) <- query(species = 'Hs', .unlist = TRUE)[, sample(unique(symbol), length(exampleRanks))]
pathway <- setNames(pathway$symbol, pathway$standard_name)
tmp <- fgsea::fgsea(pathway, exampleRanks)