Last updated: 2024-08-16
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| File | Version | Author | Date | Message |
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| Rmd | 12ca0dd | Dave Tang | 2024-08-16 | Names of gene lists |
| html | 1f77a96 | Dave Tang | 2024-08-16 | Build site. |
| Rmd | 71fae59 | Dave Tang | 2024-08-16 | Export Entrez IDs |
| html | 7f4de36 | Dave Tang | 2024-08-15 | Build site. |
| Rmd | e8b87fb | Dave Tang | 2024-08-15 | Update notebook |
| html | 4241975 | Dave Tang | 2023-12-13 | Build site. |
| Rmd | 5b0e194 | Dave Tang | 2023-12-13 | Downloading molecular signatures in R |
Following the vignette.
The molecular signatures database (MSigDB) is one of the largest collections of molecular signatures or gene expression signatures. A variety of gene expression signatures are hosted on this database including experimentally derived signatures and signatures representing pathways and ontologies from other curated databases. This rich collection of gene expression signatures (>25,000) can facilitate a wide variety of signature-based analyses, the most popular being gene set enrichment analyses. These signatures can be used to perform enrichment analysis in a DE experiment using tools such as {GSEA}, {fry} (from {limma}) and {camera} (from {limma}). Alternatively, they can be used to perform single-sample gene-set analysis of individual transcriptomic profiles using approaches such as {singscore}, {ssGSEA} and {GSVA}.
This package provides the gene sets in the MSigDB in the form of
GeneSetobjects. This data structure is specifically designed to store information about gene sets, including their member genes and metadata. Other packages, such as {msigdbr} and {EGSEAdata} provide these gene sets too, however, they do so by storing them as lists or tibbles. These structures are not specific to gene sets therefore do not allow storage of important metadata associated with each gene set, for example, their short and long descriptions. Additionally, the lack of structure allows creation of invalid gene sets. Accessory functions implemented in the {GSEABase} package provide a neat interface to interact withGeneSetobjects.
Install {msigdb}. (Dependencies are listed in the Imports section in the DESCRIPTION file.)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("msigdb", quietly = TRUE))
BiocManager::install("msigdb")Load package.
library(msigdb)
packageVersion("msigdb")[1] '1.12.0'In order to download the MSigDB database, we need to load {ExperimentHub} and {GSEABase}.
suppressPackageStartupMessages(library(ExperimentHub))
suppressPackageStartupMessages(library(GSEABase))Query an ExperimentHub object.
eh <- ExperimentHub(ask = FALSE)
AnnotationHub::query(x = eh, pattern = 'msigdb')ExperimentHub with 49 records
# snapshotDate(): 2024-04-29
# $dataprovider: Broad Institute, Emory University, EBI
# $species: Homo sapiens, Mus musculus
# $rdataclass: GSEABase::GeneSetCollection, list, data.frame
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["EH5421"]]'
title
EH5421 | msigdb.v7.2.hs.SYM
EH5422 | msigdb.v7.2.hs.EZID
EH5423 | msigdb.v7.2.mm.SYM
EH5424 | msigdb.v7.2.mm.EZID
EH6727 | MSigDB C8 MANNO MIDBRAIN
... ...
EH8296 | msigdb.v7.5.1.hs.SYM
EH8297 | msigdb.v7.5.1.mm.EZID
EH8298 | msigdb.v7.5.1.mm.idf
EH8299 | msigdb.v7.5.1.mm.SYM
EH8300 | imex_hsmm_0722 Specify a more specific pattern to look for only human collections.
AnnotationHub::query(x = eh, pattern = 'msigdb.*hs.SYM')ExperimentHub with 7 records
# snapshotDate(): 2024-04-29
# $dataprovider: Broad Institute
# $species: Homo sapiens
# $rdataclass: GSEABase::GeneSetCollection
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["EH5421"]]'
title
EH5421 | msigdb.v7.2.hs.SYM
EH6772 | msigdb.v7.3.hs.SYM
EH6778 | msigdb.v7.4.hs.SYM
EH7359 | msigdb.v7.5.hs.SYM
EH8284 | msigdb.v2022.1.hs.SYM
EH8290 | msigdb.v2023.1.hs.SYM
EH8296 | msigdb.v7.5.1.hs.SYM The experiment hubs seem to be ordered from earliest to latest.
AnnotationHub::query(x = eh, pattern = 'msigdb.*hs.SYM') |>
tail(1) -> msigdb_hs_latest
names(msigdb_hs_latest)[1] "EH8296"msigdb_hs_latestExperimentHub with 1 record
# snapshotDate(): 2024-04-29
# names(): EH8296
# package(): msigdb
# $dataprovider: Broad Institute
# $species: Homo sapiens
# $rdataclass: GSEABase::GeneSetCollection
# $rdatadateadded: 2023-07-03
# $title: msigdb.v7.5.1.hs.SYM
# $description: Gene expression signatures (Homo sapiens) from the Molecular...
# $taxonomyid: 9606
# $genome: NA
# $sourcetype: XML
# $sourceurl: https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.5...
# $sourcesize: NA
# $tags: c("Homo_sapiens_Data", "Mus_musculus_Data")
# retrieve record with 'object[["EH8296"]]' Data can be downloaded using the unique ID.
eh[[names(msigdb_hs_latest)]]Data can also be downloaded using
msigdb::getMsigdb().
msigdb_ver <- sub(pattern = "msigdb.v(.*).hs.SYM", replacement = "\\1", msigdb_hs_latest$title)
msigdb_hs_sym <- msigdb::getMsigdb(org = "hs", id = "SYM", version = msigdb_ver)see ?msigdb and browseVignettes('msigdb') for documentationloading from cachemsigdb_hs_ezid <- msigdb::getMsigdb(org = "hs", id = "EZID", version = msigdb_ver)see ?msigdb and browseVignettes('msigdb') for documentation
loading from cachemsigdb_hs_symGeneSetCollection
names: chr1p11, chr1p12, ..., GOMF_STARCH_BINDING (45226 total)
unique identifiers: RPL22P6, NBPF8, ..., POM121L15P (41072 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: BroadCollection (1 total)msigdb_hs_ezidGeneSetCollection
names: chr1p11, chr1p12, ..., GOMF_STARCH_BINDING (45226 total)
unique identifiers: 100132047, 728841, ..., 100101629 (40905 total)
types in collection:
geneIdType: EntrezIdentifier (1 total)
collectionType: BroadCollection (1 total)Save as RDS.
sym_outfile <- paste0('data/msigdb.v', msigdb_ver, '.hs.SYM.rds')
ezid_outfile <- paste0('data/msigdb.v', msigdb_ver, '.hs.EZID.rds')
saveRDS(object = msigdb_hs_sym, file = sym_outfile)
saveRDS(object = msigdb_hs_ezid, file = ezid_outfile)File size in bytes.
file.size(sym_outfile)[1] 24207930file.size(ezid_outfile)[1] 19925429Load RDS and compare.
test <- readRDS(sym_outfile)
identical(test, msigdb_hs_sym)[1] TRUEA GeneSetCollection object is effectively a list and
therefore all list processing functions work.
str(msigdb_hs_sym, max.level = 2)Formal class 'GeneSetCollection' [package "GSEABase"] with 1 slot
..@ .Data:List of 45226Each signature is stored in a GeneSet object and can be
processed using functions from the {GSEABase} package.
gs <- msigdb_hs_sym[[1984]]
gssetName: BAUS_TFF2_TARGETS_DN
geneIds: BEX4, NAT8, ..., THRSP (total: 12)
geneIdType: Symbol
collectionType: Broad
bcCategory: c2 (Curated)
bcSubCategory: CGP
details: use 'details(object)'Get gene IDs.
geneIds(gs) [1] "BEX4" "NAT8" "RBP4" "ART3" "DDX6" "PRDX2" "HEBP1" "CTSC" "PCP4"
[10] "OR2H2" "BAG2" "THRSP"Details of a gene set.
details(gs)setName: BAUS_TFF2_TARGETS_DN
geneIds: BEX4, NAT8, ..., THRSP (total: 12)
geneIdType: Symbol
collectionType: Broad
bcCategory: c2 (Curated)
bcSubCategory: CGP
setIdentifier: LVY1HGGWMJ7:35020:Fri May 26 12:20:46 2023:95704
description: Genes down-regulated in pyloric atrium with knockout of TFF2 [GeneID=7032].
(longDescription available)
organism: Mus musculus
pubMedIds: 16121031
urls: https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.5.1/msigdb_v7.5.1.xml
contributor: Arthur Liberzon
setVersion: 7.5.1
creationDate: table(sapply(lapply(msigdb_hs_sym, collectionType), bcCategory))
c1 c2 c3 c4 c5 c6 c7 c8 h
299 6180 3726 858 28005 189 5219 700 50 Number of KEGG gene lists.
table(grepl(pattern = "KEGG", x = names(msigdb_hs_sym), ignore.case = TRUE))
FALSE
45226 Create vector to subset hallmark gene sets.
wanted <- sapply(lapply(msigdb_hs_sym, collectionType), bcCategory) == "h"
table(wanted)wanted
FALSE TRUE
45176 50 Hallmark gene sets.
hallmark_gs <- msigdb_hs_sym[wanted]
hallmark_gsGeneSetCollection
names: HALLMARK_TNFA_SIGNALING_VIA_NFKB, HALLMARK_HYPOXIA, ..., HALLMARK_PANCREAS_BETA_CELLS (50 total)
unique identifiers: JUNB, CXCL2, ..., SRP14 (4383 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: BroadCollection (1 total)Genes in the HALLMARK_TNFA_SIGNALING_VIA_NFKB gene set.
geneIds(hallmark_gs[[1]]) [1] "JUNB" "CXCL2" "ATF3" "NFKBIA" "TNFAIP3" "PTGS2"
[7] "CXCL1" "IER3" "CD83" "CCL20" "CXCL3" "MAFF"
[13] "NFKB2" "TNFAIP2" "HBEGF" "KLF6" "BIRC3" "PLAUR"
[19] "ZFP36" "ICAM1" "JUN" "EGR3" "IL1B" "BCL2A1"
[25] "PPP1R15A" "ZC3H12A" "SOD2" "NR4A2" "IL1A" "RELB"
[31] "TRAF1" "BTG2" "DUSP1" "MAP3K8" "ETS2" "F3"
[37] "SDC4" "EGR1" "IL6" "TNF" "KDM6B" "NFKB1"
[43] "LIF" "PTX3" "FOSL1" "NR4A1" "JAG1" "CCL4"
[49] "GCH1" "CCL2" "RCAN1" "DUSP2" "EHD1" "IER2"
[55] "REL" "CFLAR" "RIPK2" "NFKBIE" "NR4A3" "PHLDA1"
[61] "IER5" "TNFSF9" "GEM" "GADD45A" "CXCL10" "PLK2"
[67] "BHLHE40" "EGR2" "SOCS3" "SLC2A6" "PTGER4" "DUSP5"
[73] "SERPINB2" "NFIL3" "SERPINE1" "TRIB1" "TIPARP" "RELA"
[79] "BIRC2" "CXCL6" "LITAF" "TNFAIP6" "CD44" "INHBA"
[85] "PLAU" "MYC" "TNFRSF9" "SGK1" "TNIP1" "NAMPT"
[91] "FOSL2" "PNRC1" "ID2" "CD69" "IL7R" "EFNA1"
[97] "PHLDA2" "PFKFB3" "CCL5" "YRDC" "IFNGR2" "SQSTM1"
[103] "BTG3" "GADD45B" "KYNU" "G0S2" "BTG1" "MCL1"
[109] "VEGFA" "MAP2K3" "CDKN1A" "CCN1" "TANK" "IFIT2"
[115] "IL18" "TUBB2A" "IRF1" "FOS" "OLR1" "RHOB"
[121] "AREG" "NINJ1" "ZBTB10" "PLPP3" "KLF4" "CXCL11"
[127] "SAT1" "CSF1" "GPR183" "PMEPA1" "PTPRE" "TLR2"
[133] "ACKR3" "KLF10" "MARCKS" "LAMB3" "CEBPB" "TRIP10"
[139] "F2RL1" "KLF9" "LDLR" "TGIF1" "RNF19B" "DRAM1"
[145] "B4GALT1" "DNAJB4" "CSF2" "PDE4B" "SNN" "PLEK"
[151] "STAT5A" "DENND5A" "CCND1" "DDX58" "SPHK1" "CD80"
[157] "TNFAIP8" "CCNL1" "FUT4" "CCRL2" "SPSB1" "TSC22D1"
[163] "B4GALT5" "SIK1" "CLCF1" "NFE2L2" "FOSB" "PER1"
[169] "NFAT5" "ATP2B1" "IL12B" "IL6ST" "SLC16A6" "ABCA1"
[175] "HES1" "BCL6" "IRS2" "SLC2A3" "CEBPD" "IL23A"
[181] "SMAD3" "TAP1" "MSC" "IFIH1" "IL15RA" "TNIP2"
[187] "BCL3" "PANX1" "FJX1" "EDN1" "EIF1" "BMP2"
[193] "DUSP4" "PDLIM5" "ICOSLG" "GFPT2" "KLF2" "TNC"
[199] "SERPINB8" "MXD1" Names of hallmark gene lists.
names(hallmark_gs) [1] "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
[2] "HALLMARK_HYPOXIA"
[3] "HALLMARK_CHOLESTEROL_HOMEOSTASIS"
[4] "HALLMARK_MITOTIC_SPINDLE"
[5] "HALLMARK_WNT_BETA_CATENIN_SIGNALING"
[6] "HALLMARK_TGF_BETA_SIGNALING"
[7] "HALLMARK_IL6_JAK_STAT3_SIGNALING"
[8] "HALLMARK_DNA_REPAIR"
[9] "HALLMARK_G2M_CHECKPOINT"
[10] "HALLMARK_APOPTOSIS"
[11] "HALLMARK_NOTCH_SIGNALING"
[12] "HALLMARK_ADIPOGENESIS"
[13] "HALLMARK_ESTROGEN_RESPONSE_EARLY"
[14] "HALLMARK_ESTROGEN_RESPONSE_LATE"
[15] "HALLMARK_ANDROGEN_RESPONSE"
[16] "HALLMARK_MYOGENESIS"
[17] "HALLMARK_PROTEIN_SECRETION"
[18] "HALLMARK_INTERFERON_ALPHA_RESPONSE"
[19] "HALLMARK_INTERFERON_GAMMA_RESPONSE"
[20] "HALLMARK_APICAL_JUNCTION"
[21] "HALLMARK_APICAL_SURFACE"
[22] "HALLMARK_HEDGEHOG_SIGNALING"
[23] "HALLMARK_COMPLEMENT"
[24] "HALLMARK_UNFOLDED_PROTEIN_RESPONSE"
[25] "HALLMARK_PI3K_AKT_MTOR_SIGNALING"
[26] "HALLMARK_MTORC1_SIGNALING"
[27] "HALLMARK_E2F_TARGETS"
[28] "HALLMARK_MYC_TARGETS_V1"
[29] "HALLMARK_MYC_TARGETS_V2"
[30] "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION"
[31] "HALLMARK_INFLAMMATORY_RESPONSE"
[32] "HALLMARK_XENOBIOTIC_METABOLISM"
[33] "HALLMARK_FATTY_ACID_METABOLISM"
[34] "HALLMARK_OXIDATIVE_PHOSPHORYLATION"
[35] "HALLMARK_GLYCOLYSIS"
[36] "HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY"
[37] "HALLMARK_P53_PATHWAY"
[38] "HALLMARK_UV_RESPONSE_UP"
[39] "HALLMARK_UV_RESPONSE_DN"
[40] "HALLMARK_ANGIOGENESIS"
[41] "HALLMARK_HEME_METABOLISM"
[42] "HALLMARK_COAGULATION"
[43] "HALLMARK_IL2_STAT5_SIGNALING"
[44] "HALLMARK_BILE_ACID_METABOLISM"
[45] "HALLMARK_PEROXISOME"
[46] "HALLMARK_ALLOGRAFT_REJECTION"
[47] "HALLMARK_SPERMATOGENESIS"
[48] "HALLMARK_KRAS_SIGNALING_UP"
[49] "HALLMARK_KRAS_SIGNALING_DN"
[50] "HALLMARK_PANCREAS_BETA_CELLS"
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GSEABase_1.66.0 graph_1.82.0 annotate_1.82.0
[4] XML_3.99-0.16.1 AnnotationDbi_1.66.0 IRanges_2.38.1
[7] S4Vectors_0.42.1 Biobase_2.64.0 ExperimentHub_2.12.0
[10] AnnotationHub_3.12.0 BiocFileCache_2.12.0 dbplyr_2.5.0
[13] BiocGenerics_0.50.0 msigdb_1.12.0 BiocManager_1.30.23
[16] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] KEGGREST_1.44.1 xfun_0.44 bslib_0.7.0
[4] processx_3.8.4 callr_3.7.6 vctrs_0.6.5
[7] tools_4.4.0 ps_1.7.6 generics_0.1.3
[10] curl_5.2.1 tibble_3.2.1 fansi_1.0.6
[13] RSQLite_2.3.7 blob_1.2.4 pkgconfig_2.0.3
[16] GenomeInfoDbData_1.2.12 lifecycle_1.0.4 compiler_4.4.0
[19] stringr_1.5.1 git2r_0.33.0 Biostrings_2.72.1
[22] getPass_0.2-4 GenomeInfoDb_1.40.1 httpuv_1.6.15
[25] htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.8
[28] later_1.3.2 pillar_1.9.0 crayon_1.5.2
[31] jquerylib_0.1.4 whisker_0.4.1 cachem_1.1.0
[34] mime_0.12 tidyselect_1.2.1 digest_0.6.35
[37] stringi_1.8.4 purrr_1.0.2 dplyr_1.1.4
[40] BiocVersion_3.19.1 rprojroot_2.0.4 fastmap_1.2.0
[43] cli_3.6.2 magrittr_2.0.3 utf8_1.2.4
[46] withr_3.0.0 UCSC.utils_1.0.0 filelock_1.0.3
[49] promises_1.3.0 rappdirs_0.3.3 bit64_4.0.5
[52] XVector_0.44.0 rmarkdown_2.27 httr_1.4.7
[55] bit_4.0.5 png_0.1-8 memoise_2.0.1
[58] evaluate_0.24.0 knitr_1.47 rlang_1.1.4
[61] Rcpp_1.0.12 xtable_1.8-4 glue_1.7.0
[64] DBI_1.2.3 rstudioapi_0.16.0 jsonlite_1.8.8
[67] R6_2.5.1 zlibbioc_1.50.0 fs_1.6.4