Overview
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
Please be aware that the orthologs were computationally predicted at the gene level. The full pathways may not be well conserved across species.
Installation
The package can be installed from CRAN.
install.packages("msigdbr")The package includes only a small subset of the full MSigDB database due to CRAN size limitations. Please install the msigdbdf package to access the full MSigDB database:
install.packages("msigdbdf", repos = "https://igordot.r-universe.dev")Usage
Load package.
All gene sets in the database can be retrieved by specifying a species of interest.
all_gene_sets <- msigdbr(species = "Mus musculus")
head(all_gene_sets)
#> # A tibble: 6 × 22
#> gene_symbol ncbi_gene ensembl_gene db_gene_sy…¹ db_ncbi_gene db_ensembl…²
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 Abcc4 239273 ENSMUSG000000328… ABCC4 10257 ENSG0000012…
#> 2 Abraxas2 109359 ENSMUSG000000309… ABRAXAS2 23172 ENSG0000016…
#> 3 Actn4 60595 ENSMUSG000000548… ACTN4 81 ENSG0000013…
#> 4 Acvr1 11477 ENSMUSG000000268… ACVR1 90 ENSG0000011…
#> 5 Adam9 11502 ENSMUSG000000315… ADAM9 8754 ENSG0000016…
#> 6 Adamts5 23794 ENSMUSG000000228… ADAMTS5 11096 ENSG0000015…
#> # ℹ abbreviated names: ¹db_gene_symbol, ²db_ensembl_gene
#> # ℹ 16 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_url <chr>, db_version <chr>, db_target_species <chr>,
#> # ortholog_taxon_id <int>, ortholog_sources <chr>, num_ortholog_sources <dbl>You can retrieve data just for a specific collection, such as the Hallmark gene sets.
h_gene_sets <- msigdbr(species = "mouse", collection = "H")
head(h_gene_sets)
#> # A tibble: 6 × 22
#> gene_symbol ncbi_gene ensembl_gene db_gene_sy…¹ db_ncbi_gene db_ensembl…²
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 Abca1 11303 ENSMUSG000000152… ABCA1 19 ENSG0000016…
#> 2 Abcb8 74610 ENSMUSG000000289… ABCB8 11194 ENSG0000019…
#> 3 Acaa2 52538 ENSMUSG000000368… ACAA2 10449 ENSG0000016…
#> 4 Acadl 11363 ENSMUSG000000260… ACADL 33 ENSG0000011…
#> 5 Acadm 11364 ENSMUSG000000629… ACADM 34 ENSG0000011…
#> 6 Acads 11409 ENSMUSG000000295… ACADS 35 ENSG0000012…
#> # ℹ abbreviated names: ¹db_gene_symbol, ²db_ensembl_gene
#> # ℹ 16 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_url <chr>, db_version <chr>, db_target_species <chr>,
#> # ortholog_taxon_id <int>, ortholog_sources <chr>, num_ortholog_sources <dbl>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")
head(cgp_gene_sets)
#> # A tibble: 6 × 22
#> gene_symbol ncbi_gene ensembl_gene db_gene_sy…¹ db_ncbi_gene db_ensembl…²
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 Ahnak 66395 ENSMUSG000000698… AHNAK 79026 ENSG0000012…
#> 2 Alcam 11658 ENSMUSG000000226… ALCAM 214 ENSG0000017…
#> 3 Ankrd40 71452 ENSMUSG000000208… ANKRD40 91369 ENSG0000015…
#> 4 Arid1a 93760 ENSMUSG000000078… ARID1A 8289 ENSG0000011…
#> 5 Bckdhb 12040 ENSMUSG000000322… BCKDHB 594 ENSG0000008…
#> 6 AU021092 239691 ENSMUSG000000516… C16orf89 146556 ENSG0000015…
#> # ℹ abbreviated names: ¹db_gene_symbol, ²db_ensembl_gene
#> # ℹ 16 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_url <chr>, db_version <chr>, db_target_species <chr>,
#> # ortholog_taxon_id <int>, ortholog_sources <chr>, num_ortholog_sources <dbl>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.
all_gene_sets |>
dplyr::filter(gs_collection == "H") |>
head()
#> # A tibble: 6 × 22
#> gene_symbol ncbi_gene ensembl_gene db_gene_sy…¹ db_ncbi_gene db_ensembl…²
#> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 Abca1 11303 ENSMUSG000000152… ABCA1 19 ENSG0000016…
#> 2 Abcb8 74610 ENSMUSG000000289… ABCB8 11194 ENSG0000019…
#> 3 Acaa2 52538 ENSMUSG000000368… ACAA2 10449 ENSG0000016…
#> 4 Acadl 11363 ENSMUSG000000260… ACADL 33 ENSG0000011…
#> 5 Acadm 11364 ENSMUSG000000629… ACADM 34 ENSG0000011…
#> 6 Acads 11409 ENSMUSG000000295… ACADS 35 ENSG0000012…
#> # ℹ abbreviated names: ¹db_gene_symbol, ²db_ensembl_gene
#> # ℹ 16 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_url <chr>, db_version <chr>, db_target_species <chr>,
#> # ortholog_taxon_id <int>, ortholog_sources <chr>, num_ortholog_sources <dbl>The version of the MSigDB database is stored in the
db_version column of the returned data frame.
unique(all_gene_sets$db_version)
#> [1] "2024.1.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… 3494
#> 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" 1736
#> 9 C2 "CP:WIKIPATHWAYS" "WikiPathways" 830
#> 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" 7608
#> 18 C5 "GO:CC" "GO Cellular Component" 1026
#> 19 C5 "GO:MF" "GO Molecular Function" 1820
#> 20 C5 "HPO" "Human Phenotype Ontology" 5653
#> 21 C6 "" "Oncogenic Signature" 189
#> 22 C7 "IMMUNESIGDB" "ImmuneSigDB" 4872
#> 23 C7 "VAX" "HIPC Vaccine Response" 347
#> 24 C8 "" "Cell Type Signature" 840
#> 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$gs_name)
gsva(gset.idx.list = msigdbr_list, ...)Potential questions and concerns
Which version of MSigDB was used?
The MSigDB version is stored in the db_version column of
the returned data frame. You can check the version used with
unique(msigdbr_df$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 MSigDB gene sets in R. You don’t need to download the GMT files and import them. You don’t need to learn how to restructure the output to make it compatible with downstream tools. You don’t need to convert the genes to your organism if you are working with non-human data.
Can I convert between human and mouse genes just by adjusting gene capitalization?
That will work for most, but not all, genes.
Can I convert human genes to any organism myself instead of using this package?
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.
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. WEHI provides MSigDB gene sets in R format for human and mouse. MSigDF and a more recent ToledoEM/msigdf fork provide a tidyverse-friendly data frame. These are updated at varying frequencies and may not be based on the latest version of MSigDB. Since 2022, the GSEA/MSigDB team provides collections that are natively mouse and don’t require orthology conversion.
What if I have other questions?
You can submit feedback and report bugs on GitHub.
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 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 provided by HUGO Gene Nomenclature Committee at the European Bioinformatics Institute which integrates the orthology assertions predicted for human genes 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").