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Introduction to msigdbr

Source: vignettes/articles/msigdbr-intro.Rmd
msigdbr-intro.Rmd

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

Installation

The package can be installed from CRAN.

install.packages("msigdbr")

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)

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)

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(species = "Mus musculus", db_species = "MM")
head(all_mm_gene_sets)

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_symbol Official gene symbol for the requested species.
  • ncbi_gene NCBI (formerly Entrez) ID for the requested species.
  • ensembl_gene Ensembl ID for the requested species.
  • db_gene_symbol Official gene symbol in the MSigDB database.
  • db_ncbi_gene NCBI ID in the MSigDB database.
  • db_ensembl_gene Ensembl ID in the MSigDB database.
  • source_gene Gene identifier in the original publication.
  • ortholog_taxon_id The taxon ID of the species.
  • ortholog_sources The databases that support the ortholog mapping.
  • num_ortholog_sources The number of databases that support the ortholog mapping.

The gs_* columns provide details about the gene sets:

  • gs_id Gene set systematic name.
  • gs_name Gene set standard name.
  • gs_collection The collection the gene set belongs to.
  • gs_subcollection The sub-collection the gene set belongs to.
  • gs_collection_name The name of the collection.
  • gs_description A description of the gene set.
  • gs_source_species The species from which the gene set originated from.
  • gs_pmid PubMed ID.
  • gs_geoid GEO ID.
  • gs_exact_source The original source of the gene set, such as a resource identifier, a figure, or a supplementary document from a publication.
  • gs_url A 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_version The version of the MSigDB database.
  • db_target_species The 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.

Use msigdbr_collections() to check the available collections.

Pathway 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 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. This package includes Ensembl IDs. 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 simply 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. 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.

Since 2022, the GSEA/MSigDB team provides collections that are natively mouse and don’t require orthology conversion.

What if I have other questions?

Please 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").