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Introduction to msigdbr

Source: vignettes/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 and requiring an active internet connection

Please be aware that the homologs were computationally predicted for distinct genes. The full pathways may not be well conserved across species.

Installation

The package can be installed from CRAN.

install.packages("msigdbr")

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 × 18
#>   gs_cat gs_subcat     gs_name gene_symbol entrez_gene ensembl_gene human_gene…¹
#>   <chr>  <chr>         <chr>   <chr>             <int> <chr>        <chr>       
#> 1 C3     MIR:MIR_Lega… AAACCA… Abcc4            239273 ENSMUSG0000… ABCC4       
#> 2 C3     MIR:MIR_Lega… AAACCA… Abraxas2         109359 ENSMUSG0000… ABRAXAS2    
#> 3 C3     MIR:MIR_Lega… AAACCA… Actn4             60595 ENSMUSG0000… ACTN4       
#> 4 C3     MIR:MIR_Lega… AAACCA… Acvr1             11477 ENSMUSG0000… ACVR1       
#> 5 C3     MIR:MIR_Lega… AAACCA… Adam9             11502 ENSMUSG0000… ADAM9       
#> 6 C3     MIR:MIR_Lega… AAACCA… Adamts5           23794 ENSMUSG0000… ADAMTS5     
#> # … with 11 more variables: human_entrez_gene <int>, human_ensembl_gene <chr>,
#> #   gs_id <chr>, gs_pmid <chr>, gs_geoid <chr>, gs_exact_source <chr>,
#> #   gs_url <chr>, gs_description <chr>, taxon_id <int>, ortholog_sources <chr>,
#> #   num_ortholog_sources <dbl>, and abbreviated variable name
#> #   ¹​human_gene_symbol

You can retrieve data just for a specific collection/category, such as the hallmark gene sets.

h_gene_sets <- msigdbr(species = "mouse", category = "H")
head(h_gene_sets)
#> # A tibble: 6 × 18
#>   gs_cat gs_subcat gs_name     gene_symbol entrez_gene ensembl_gene human_gene…¹
#>   <chr>  <chr>     <chr>       <chr>             <int> <chr>        <chr>       
#> 1 H      ""        HALLMARK_A… Abca1             11303 ENSMUSG0000… ABCA1       
#> 2 H      ""        HALLMARK_A… Abcb8             74610 ENSMUSG0000… ABCB8       
#> 3 H      ""        HALLMARK_A… Acaa2             52538 ENSMUSG0000… ACAA2       
#> 4 H      ""        HALLMARK_A… Acadl             11363 ENSMUSG0000… ACADL       
#> 5 H      ""        HALLMARK_A… Acadm             11364 ENSMUSG0000… ACADM       
#> 6 H      ""        HALLMARK_A… Acads             11409 ENSMUSG0000… ACADS       
#> # … with 11 more variables: human_entrez_gene <int>, human_ensembl_gene <chr>,
#> #   gs_id <chr>, gs_pmid <chr>, gs_geoid <chr>, gs_exact_source <chr>,
#> #   gs_url <chr>, gs_description <chr>, taxon_id <int>, ortholog_sources <chr>,
#> #   num_ortholog_sources <dbl>, and abbreviated variable name
#> #   ¹​human_gene_symbol

You can specify a sub-category, such as C2 (curated) CGP (chemical and genetic perturbations) gene sets.

cgp_gene_sets <- msigdbr(species = "mouse", category = "C2", subcategory = "CGP")
head(cgp_gene_sets)
#> # A tibble: 6 × 18
#>   gs_cat gs_subcat gs_name     gene_symbol entrez_gene ensembl_gene human_gene…¹
#>   <chr>  <chr>     <chr>       <chr>             <int> <chr>        <chr>       
#> 1 C2     CGP       ABBUD_LIF_… Ahnak             66395 ENSMUSG0000… AHNAK       
#> 2 C2     CGP       ABBUD_LIF_… Alcam             11658 ENSMUSG0000… ALCAM       
#> 3 C2     CGP       ABBUD_LIF_… Ankrd40           71452 ENSMUSG0000… ANKRD40     
#> 4 C2     CGP       ABBUD_LIF_… Arid1a            93760 ENSMUSG0000… ARID1A      
#> 5 C2     CGP       ABBUD_LIF_… Bckdhb            12040 ENSMUSG0000… BCKDHB      
#> 6 C2     CGP       ABBUD_LIF_… AU021092         239691 ENSMUSG0000… C16orf89    
#> # … with 11 more variables: human_entrez_gene <int>, human_ensembl_gene <chr>,
#> #   gs_id <chr>, gs_pmid <chr>, gs_geoid <chr>, gs_exact_source <chr>,
#> #   gs_url <chr>, gs_description <chr>, taxon_id <int>, ortholog_sources <chr>,
#> #   num_ortholog_sources <dbl>, and abbreviated variable name
#> #   ¹​human_gene_symbol

If you require more custom 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/category using dplyr::filter().

all_gene_sets %>%
  dplyr::filter(gs_cat == "H") %>%
  head()
#> # A tibble: 6 × 18
#>   gs_cat gs_subcat gs_name     gene_symbol entrez_gene ensembl_gene human_gene…¹
#>   <chr>  <chr>     <chr>       <chr>             <int> <chr>        <chr>       
#> 1 H      ""        HALLMARK_A… Abca1             11303 ENSMUSG0000… ABCA1       
#> 2 H      ""        HALLMARK_A… Abcb8             74610 ENSMUSG0000… ABCB8       
#> 3 H      ""        HALLMARK_A… Acaa2             52538 ENSMUSG0000… ACAA2       
#> 4 H      ""        HALLMARK_A… Acadl             11363 ENSMUSG0000… ACADL       
#> 5 H      ""        HALLMARK_A… Acadm             11364 ENSMUSG0000… ACADM       
#> 6 H      ""        HALLMARK_A… Acads             11409 ENSMUSG0000… ACADS       
#> # … with 11 more variables: human_entrez_gene <int>, human_ensembl_gene <chr>,
#> #   gs_id <chr>, gs_pmid <chr>, gs_geoid <chr>, gs_exact_source <chr>,
#> #   gs_url <chr>, gs_description <chr>, taxon_id <int>, ortholog_sources <chr>,
#> #   num_ortholog_sources <dbl>, and abbreviated variable name
#> #   ¹​human_gene_symbol

Helper functions

There are msigdbr_species() and msigdbr_collections() helper functions to assist with setting the msigdbr() parameters.

You can check the available species with msigdbr_species(). Either scientific or 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 frog

You can check the available collections with msigdbr_collections().

msigdbr_collections()
#> # A tibble: 23 × 3
#>    gs_cat gs_subcat         num_genesets
#>    <chr>  <chr>                    <int>
#>  1 C1     ""                         299
#>  2 C2     "CGP"                     3399
#>  3 C2     "CP"                        29
#>  4 C2     "CP:BIOCARTA"              292
#>  5 C2     "CP:KEGG"                  186
#>  6 C2     "CP:PID"                   196
#>  7 C2     "CP:REACTOME"             1635
#>  8 C2     "CP:WIKIPATHWAYS"          712
#>  9 C3     "MIR:MIR_Legacy"           221
#> 10 C3     "MIR:MIRDB"               2377
#> 11 C3     "TFT:GTRD"                 517
#> 12 C3     "TFT:TFT_Legacy"           610
#> 13 C4     "CGN"                      427
#> 14 C4     "CM"                       431
#> 15 C5     "GO:BP"                   7763
#> 16 C5     "GO:CC"                   1035
#> 17 C5     "GO:MF"                   1763
#> 18 C5     "HPO"                     5142
#> 19 C6     ""                         189
#> 20 C7     "IMMUNESIGDB"             4872
#> 21 C7     "VAX"                      347
#> 22 C8     ""                         704
#> 23 H      ""                          50

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 Entrez Gene IDs.

msigdbr_t2g <- msigdbr_df %>%
  dplyr::distinct(gs_name, entrez_gene) %>%
  as.data.frame()
enricher(gene = gene_ids_vector, TERM2GENE = msigdbr_t2g, ...)

Use the gene sets data frame for clusterProfiler with genes as gene symbols.

msigdbr_t2g <- msigdbr_df %>%
  dplyr::distinct(gs_name, gene_symbol) %>%
  as.data.frame()
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 or concerns

Which version of MSigDB was used?

This package was generated with MSigDB v7.5.1. The MSigDB version is used as the base of the msigdbr CRAN package version. You can check the installed version with packageVersion("msigdbr").

Can I download the gene sets directly from MSigDB instead of using this package?

Yes. You can then import the GMT files (with getGmt() from the GSEABase package, for example). The GMTs only include the human genes, even for gene sets generated from mouse experiments. If you are working with non-human data, you then have to convert the MSigDB genes to your organism or your genes to human.

Can I convert between human and mouse genes just by adjusting gene capitalization?

That will work for most genes, but not all.

Can I convert human genes to any organism myself instead of using this package?

Yes. A 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 relies on the WEHI resource, but is converted to a more tidyverse-friendly data frame. There is a more recent ToledoEM/msigdf fork. 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), 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 cite an R package such as msigdbr, you can execute citation("msigdbr").