About
Pathway analysis is a common task in genomics research and there are many available R-based software tools. Depending on the tool, it may be necessary to import the pathways, translate genes to the appropriate species, convert between symbols and IDs, and format the resulting object.
The msigdbr R package provides Molecular Signatures Database (MSigDB) gene sets typically used with the Gene Set Enrichment Analysis (GSEA) software:
- in an R-friendly “tidy” format with one gene pair per row
- for multiple frequently studied model organisms, such as mouse, rat, pig, zebrafish, fly, and yeast, in addition to the original human genes
- as gene symbols as well as NCBI Entrez and Ensembl IDs
- without accessing external resources requiring an active internet connection
Usage
Basic usage
Load package.
The msigdbr() function retrieves a data frame of all
genes and gene sets in the database.
all_gene_sets <- msigdbr()
head(all_gene_sets)
#> # A tibble: 6 × 20
#> gene_symbol ncbi_gene ensembl_gene db_gene_symbol db_ncbi_gene db_ensembl_gene
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 ABCC4 10257 ENSG0000012… ABCC4 10257 ENSG00000125257
#> 2 ABRAXAS2 23172 ENSG0000016… ABRAXAS2 23172 ENSG00000165660
#> 3 ACTN4 81 ENSG0000013… ACTN4 81 ENSG00000130402
#> 4 ACVR1 90 ENSG0000011… ACVR1 90 ENSG00000115170
#> 5 ADAM9 8754 ENSG0000016… ADAM9 8754 ENSG00000168615
#> 6 ADAMTS5 11096 ENSG0000015… ADAMTS5 11096 ENSG00000154736
#> # ℹ 14 more variables: source_gene <chr>, gs_id <chr>, gs_name <chr>,
#> # gs_collection <chr>, gs_subcollection <chr>, gs_collection_name <chr>,
#> # gs_description <chr>, gs_source_species <chr>, gs_pmid <chr>,
#> # gs_geoid <chr>, gs_exact_source <chr>, gs_url <chr>, db_version <chr>,
#> # db_target_species <chr>Species
The species parameter enables conversion of the original
human genes to their orthologs in various model organisms, such as
mouse. You can use msigdbr_species() to check the available
species.
all_gene_sets <- msigdbr(species = "Mus musculus")
head(all_gene_sets)
#> # A tibble: 6 × 23
#> gene_symbol ncbi_gene ensembl_gene db_gene_symbol db_ncbi_gene db_ensembl_gene
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Abcc4 239273 ENSMUSG0000… ABCC4 10257 ENSG00000125257
#> 2 Abraxas2 109359 ENSMUSG0000… ABRAXAS2 23172 ENSG00000165660
#> 3 Actn4 60595 ENSMUSG0000… ACTN4 81 ENSG00000130402
#> 4 Acvr1 11477 ENSMUSG0000… ACVR1 90 ENSG00000115170
#> 5 Adam9 11502 ENSMUSG0000… ADAM9 8754 ENSG00000168615
#> 6 Adamts5 23794 ENSMUSG0000… ADAMTS5 11096 ENSG00000154736
#> # ℹ 17 more variables: source_gene <chr>, gs_id <chr>, gs_name <chr>,
#> # gs_collection <chr>, gs_subcollection <chr>, gs_collection_name <chr>,
#> # gs_description <chr>, gs_source_species <chr>, gs_pmid <chr>,
#> # gs_geoid <chr>, gs_exact_source <chr>, gs_url <chr>, db_version <chr>,
#> # db_target_species <chr>, ortholog_taxon_id <int>, ortholog_sources <chr>,
#> # num_ortholog_sources <dbl>Please be aware that the orthologs are computationally predicted at the gene level. The full pathways may not be well conserved across species.
There are human and mouse versions of MSigDB. The
db_species parameter specifies the database (human by
default).
all_mm_gene_sets <- msigdbr(db_species = "MM", species = "Mus musculus")
head(all_mm_gene_sets)
#> # A tibble: 6 × 20
#> gene_symbol ncbi_gene ensembl_gene db_gene_symbol db_ncbi_gene db_ensembl_gene
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 AU021092 239691 ENSMUSG0000… AU021092 239691 ENSMUSG0000005…
#> 2 Ahnak 66395 ENSMUSG0000… Ahnak 66395 ENSMUSG0000006…
#> 3 Alcam 11658 ENSMUSG0000… Alcam 11658 ENSMUSG0000002…
#> 4 Ankrd40 71452 ENSMUSG0000… Ankrd40 71452 ENSMUSG0000002…
#> 5 Arid1a 93760 ENSMUSG0000… Arid1a 93760 ENSMUSG0000000…
#> 6 Bckdhb 12040 ENSMUSG0000… Bckdhb 12040 ENSMUSG0000003…
#> # ℹ 14 more variables: source_gene <chr>, gs_id <chr>, gs_name <chr>,
#> # gs_collection <chr>, gs_subcollection <chr>, gs_collection_name <chr>,
#> # gs_description <chr>, gs_source_species <chr>, gs_pmid <chr>,
#> # gs_geoid <chr>, gs_exact_source <chr>, gs_url <chr>, db_version <chr>,
#> # db_target_species <chr>The genes within each gene set may originate from a species different
from the database target species, as indicated by the
gs_source_species and db_target_species
fields.
Collections
You can retrieve data just for a specific collection, such as the Hallmark gene sets.
h_gene_sets <- msigdbr(species = "mouse", collection = "H")You can specify a sub-collection, such as C2 (curated) CGP (chemical and genetic perturbations) gene sets.
cgp_gene_sets <- msigdbr(species = "mouse", collection = "C2", subcollection = "CGP")If you require more precise filtering, the msigdbr()
function output is a data frame that can be manipulated using standard
methods. For example, you can subset to a specific collection using
dplyr.
dplyr::filter(all_gene_sets, gs_collection == "H")Output
The msigdbr() function returns a data frame with one
gene per row. Generally, the gene_symbol (gene symbol) and
gs_name (gene set name) columns are the most useful.
The data frame contains the following columns for gene-level info:
-
gene_symbolOfficial gene symbol for the requested species. -
ncbi_geneNCBI (formerly Entrez) ID for the requested species. -
ensembl_geneEnsembl ID for the requested species. -
db_gene_symbolOfficial gene symbol in the MSigDB database. -
db_ncbi_geneNCBI ID in the MSigDB database. -
db_ensembl_geneEnsembl ID in the MSigDB database. -
source_geneGene identifier in the original publication. -
ortholog_taxon_idThe taxon ID of the species. -
ortholog_sourcesThe databases that support the ortholog mapping. -
num_ortholog_sourcesThe number of databases that support the ortholog mapping.
The gs_* columns provide details about the gene
sets:
-
gs_idGene set systematic name. -
gs_nameGene set standard name. -
gs_collectionThe collection the gene set belongs to. -
gs_subcollectionThe sub-collection the gene set belongs to. -
gs_collection_nameThe name of the collection. -
gs_descriptionA description of the gene set. -
gs_source_speciesThe species from which the gene set originated from. -
gs_pmidPubMed ID. -
gs_geoidGEO ID. -
gs_exact_sourceThe original source of the gene set, such as a resource identifier, a figure, or a supplementary document from a publication. -
gs_urlA URL to the original source of the gene set.
The db_* columns provide details about the MSigDB
database and should be identical for the entire data frame:
-
db_versionThe version of the MSigDB database. -
db_target_speciesThe target species of the MSigDB database (HS or MM).
unique(all_gene_sets$db_version)
#> [1] "2025.1.Hs"Helper functions
There are helper functions to assist with setting the
msigdbr() parameters.
Use msigdbr_species() to check the available species.
Both scientific and common names are acceptable for the
msigdbr() function.
msigdbr_species()
#> # A tibble: 20 × 2
#> species_name species_common_name
#> <chr> <chr>
#> 1 Anolis carolinensis Carolina anole, green anole
#> 2 Bos taurus bovine, cattle, cow, dairy cow, domestic cat…
#> 3 Caenorhabditis elegans NA
#> 4 Canis lupus familiaris dog, dogs
#> 5 Danio rerio leopard danio, zebra danio, zebra fish, zebr…
#> 6 Drosophila melanogaster fruit fly
#> 7 Equus caballus domestic horse, equine, horse
#> 8 Felis catus cat, cats, domestic cat
#> 9 Gallus gallus bantam, chicken, chickens, Gallus domesticus
#> 10 Homo sapiens human
#> 11 Macaca mulatta rhesus macaque, rhesus macaques, Rhesus monk…
#> 12 Monodelphis domestica gray short-tailed opossum
#> 13 Mus musculus house mouse, mouse
#> 14 Ornithorhynchus anatinus duck-billed platypus, duckbill platypus, pla…
#> 15 Pan troglodytes chimpanzee
#> 16 Rattus norvegicus brown rat, Norway rat, rat, rats
#> 17 Saccharomyces cerevisiae baker's yeast, brewer's yeast, S. cerevisiae
#> 18 Schizosaccharomyces pombe 972h- NA
#> 19 Sus scrofa pig, pigs, swine, wild boar
#> 20 Xenopus tropicalis tropical clawed frog, western clawed frogUse msigdbr_collections() to check the available
collections.
msigdbr_collections()
#> # A tibble: 25 × 4
#> gs_collection gs_subcollection gs_collection_name num_genesets
#> <chr> <chr> <chr> <int>
#> 1 C1 "" "Positional" 302
#> 2 C2 "CGP" "Chemical and Genetic Perturbat… 3538
#> 3 C2 "CP" "Canonical Pathways" 19
#> 4 C2 "CP:BIOCARTA" "BioCarta Pathways" 292
#> 5 C2 "CP:KEGG_LEGACY" "KEGG Legacy Pathways" 186
#> 6 C2 "CP:KEGG_MEDICUS" "KEGG Medicus Pathways" 658
#> 7 C2 "CP:PID" "PID Pathways" 196
#> 8 C2 "CP:REACTOME" "Reactome Pathways" 1787
#> 9 C2 "CP:WIKIPATHWAYS" "WikiPathways" 885
#> 10 C3 "MIR:MIRDB" "miRDB" 2377
#> 11 C3 "MIR:MIR_LEGACY" "MIR_Legacy" 221
#> 12 C3 "TFT:GTRD" "GTRD" 505
#> 13 C3 "TFT:TFT_LEGACY" "TFT_Legacy" 610
#> 14 C4 "3CA" "Curated Cancer Cell Atlas gene… 148
#> 15 C4 "CGN" "Cancer Gene Neighborhoods" 427
#> 16 C4 "CM" "Cancer Modules" 431
#> 17 C5 "GO:BP" "GO Biological Process" 7583
#> 18 C5 "GO:CC" "GO Cellular Component" 1042
#> 19 C5 "GO:MF" "GO Molecular Function" 1855
#> 20 C5 "HPO" "Human Phenotype Ontology" 5748
#> 21 C6 "" "Oncogenic Signature" 189
#> 22 C7 "IMMUNESIGDB" "ImmuneSigDB" 4872
#> 23 C7 "VAX" "HIPC Vaccine Response" 347
#> 24 C8 "" "Cell Type Signature" 866
#> 25 H "" "Hallmark" 50Pathway enrichment analysis
The msigdbr output can be used with various pathway analysis packages.
Use the gene sets data frame for clusterProfiler with genes as NCBI/Entrez IDs.
msigdbr_t2g <- dplyr::distinct(msigdbr_df, gs_name, ncbi_gene)
enricher(gene = gene_ids_vector, TERM2GENE = msigdbr_t2g, ...)Use the gene sets data frame for clusterProfiler with genes as gene symbols.
msigdbr_t2g <- dplyr::distinct(msigdbr_df, gs_name, gene_symbol)
enricher(gene = gene_symbols_vector, TERM2GENE = msigdbr_t2g, ...)Use the gene sets data frame for fgsea.
msigdbr_list <- split(x = msigdbr_df$gene_symbol, f = msigdbr_df$gs_name)
fgsea(pathways = msigdbr_list, ...)Use the gene sets data frame for GSVA.
msigdbr_list <- split(x = msigdbr_df$gene_symbol, f = msigdbr_df$gs_name)
gsvapar <- gsvaParam(geneSets = msigdbr_list, ...)
gsva(gsvapar)Earlier versions of GSVA (<1.50) only need the gsva()
function.
msigdbr_list <- split(x = msigdbr_df$gene_symbol, f = msigdbr_df$gs_name)
gsva(gset.idx.list = msigdbr_list, ...)Potential questions and concerns
Which version of MSigDB was used?
The MSigDB version is included in the returned data frame. Check the
version with unique(msigdbr_df$db_version).
Why use this package when I can download the gene sets directly from MSigDB?
This package makes it more convenient to work with the MSigDB gene sets in R. You don’t need to import the GMT files and restructure the content to make it compatible with downstream tools. It adds Ensembl gene IDs and converts the gene symbols/IDs if you are working with non-human data.
Can I convert between human and mouse genes simply by adjusting gene capitalization?
That will work for most, but not all, genes. Since 2022, the GSEA/MSigDB team provides collections that are natively mouse and don’t require orthology conversion.
Can I convert genes to any organism myself?
One popular method is using the biomaRt package. You may still end up with dozens of homologs for some genes, so additional cleanup may be helpful. Because biomaRt relies on live BioMart services, it can be disrupted by server outage or maintenance.
Aren’t there already other similar tools?
There are a few resources that provide some of the msigdbr functionality and served as an inspiration for this package. EGSEAdata Bioconductor package provides data as a list. WEHI MSigDB provides data for human and mouse as RDS files. MSigDF provides data as an R data frame. These are updated at varying frequencies and may not be based on the latest version of MSigDB.
What if I have other questions?
Please post questions or feedback on GitHub Discussions. Report bugs on GitHub Issues.
Details
The Molecular Signatures Database (MSigDB) is a collection of gene sets originally created for use with the Gene Set Enrichment Analysis (GSEA) software. To cite the use of the underlying MSigDB data, reference Subramanian, Tamayo, et al. (2005, PNAS) and one or more of the following as appropriate: Liberzon, et al. (2011, Bioinformatics), Liberzon, et al. (2015, Cell Systems), Castanza, et al. (2023, Nature Methods) and also the source for the gene set.
Gene homologs are mapped using the babelgene package. The information is sourced from the HUGO Gene Nomenclature Committee at the European Bioinformatics Institute which integrates the orthology assertions predicted by eggNOG, Ensembl Compara, HGNC, HomoloGene, Inparanoid, NCBI Gene Orthology, OMA, OrthoDB, OrthoMCL, Panther, PhylomeDB, TreeFam and ZFIN. For each human equivalent within each species, only the ortholog supported by the largest number of databases is used.
For information on how to cite the msigdbr R package, run
citation("msigdbr").