# Introduction to msigdbr

## 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/long format with one gene 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
- that can be installed and loaded as a package without requiring additional external files

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.

    library(msigdbr)

All gene sets in the database can be retrieved without specifying a collection/category.

    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_ge… human_gene… human_entr…
    #>   <chr>  <chr>         <chr>   <chr>             <int> <chr>       <chr>             <int>
    #> 1 C3     MIR:MIR_Lega… AAACCA… Abcc4            239273 ENSMUSG000… ABCC4             10257
    #> 2 C3     MIR:MIR_Lega… AAACCA… Abraxas2         109359 ENSMUSG000… ABRAXAS2          23172
    #> 3 C3     MIR:MIR_Lega… AAACCA… Actn4             60595 ENSMUSG000… ACTN4                81
    #> 4 C3     MIR:MIR_Lega… AAACCA… Acvr1             11477 ENSMUSG000… ACVR1                90
    #> 5 C3     MIR:MIR_Lega… AAACCA… Adam9             11502 ENSMUSG000… ADAM9              8754
    #> 6 C3     MIR:MIR_Lega… AAACCA… Adamts5           23794 ENSMUSG000… ADAMTS5           11096
    #> # … with 10 more variables: 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>

There is a helper function to show the available species. Either scientific or common names are acceptable.

    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 cattle, domes…
    #>  3 Caenorhabditis elegans          <NA>
    #>  4 Canis lupus familiaris          dog, dogs
    #>  5 Danio rerio                     leopard danio, zebra danio, zebra fish, zebrafish
    #>  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 monkey, rhesus…
    #> 12 Monodelphis domestica           gray short-tailed opossum
    #> 13 Mus musculus                    house mouse, mouse
    #> 14 Ornithorhynchus anatinus        duck-billed platypus, duckbill platypus, platypus
    #> 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 retrieve data for a specific collection, 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_ge… human_gene… human_entr…
    #>   <chr>  <chr>     <chr>       <chr>             <int> <chr>       <chr>             <int>
    #> 1 H      ""        HALLMARK_A… Abca1             11303 ENSMUSG000… ABCA1                19
    #> 2 H      ""        HALLMARK_A… Abcb8             74610 ENSMUSG000… ABCB8             11194
    #> 3 H      ""        HALLMARK_A… Acaa2             52538 ENSMUSG000… ACAA2             10449
    #> 4 H      ""        HALLMARK_A… Acadl             11363 ENSMUSG000… ACADL                33
    #> 5 H      ""        HALLMARK_A… Acadm             11364 ENSMUSG000… ACADM                34
    #> 6 H      ""        HALLMARK_A… Acads             11409 ENSMUSG000… ACADS                35
    #> # … with 10 more variables: 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>

Retrieve mouse 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_ge… human_gene… human_entr…
    #>   <chr>  <chr>     <chr>       <chr>             <int> <chr>       <chr>             <int>
    #> 1 C2     CGP       ABBUD_LIF_… Ahnak             66395 ENSMUSG000… AHNAK             79026
    #> 2 C2     CGP       ABBUD_LIF_… Alcam             11658 ENSMUSG000… ALCAM               214
    #> 3 C2     CGP       ABBUD_LIF_… Ankrd40           71452 ENSMUSG000… ANKRD40           91369
    #> 4 C2     CGP       ABBUD_LIF_… Arid1a            93760 ENSMUSG000… ARID1A             8289
    #> 5 C2     CGP       ABBUD_LIF_… Bckdhb            12040 ENSMUSG000… BCKDHB              594
    #> 6 C2     CGP       ABBUD_LIF_… AU021092         239691 ENSMUSG000… C16orf89         146556
    #> # … with 10 more variables: 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>

There is a helper function to show the available collections.

    msigdbr_collections()
    #> # A tibble: 23 × 3
    #>    gs_cat gs_subcat         num_genesets
    #>    <chr>  <chr>                    <int>
    #>  1 C1     ""                         299
    #>  2 C2     "CGP"                     3384
    #>  3 C2     "CP"                        29
    #>  4 C2     "CP:BIOCARTA"              292
    #>  5 C2     "CP:KEGG"                  186
    #>  6 C2     "CP:PID"                   196
    #>  7 C2     "CP:REACTOME"             1615
    #>  8 C2     "CP:WIKIPATHWAYS"          664
    #>  9 C3     "MIR:MIRDB"               2377
    #> 10 C3     "MIR:MIR_Legacy"           221
    #> 11 C3     "TFT:GTRD"                 518
    #> 12 C3     "TFT:TFT_Legacy"           610
    #> 13 C4     "CGN"                      427
    #> 14 C4     "CM"                       431
    #> 15 C5     "GO:BP"                   7658
    #> 16 C5     "GO:CC"                   1006
    #> 17 C5     "GO:MF"                   1738
    #> 18 C5     "HPO"                     5071
    #> 19 C6     ""                         189
    #> 20 C7     "IMMUNESIGDB"             4872
    #> 21 C7     "VAX"                      347
    #> 22 C8     ""                         700
    #> 23 H      ""                          50

The `msigdbr()` function output is a data frame and can be manipulated using more standard methods.

    all_gene_sets %>%
      dplyr::filter(gs_cat == "H") %>%
      head()
    #> # A tibble: 6 × 18
    #>   gs_cat gs_subcat gs_name     gene_symbol entrez_gene ensembl_ge… human_gene… human_entr…
    #>   <chr>  <chr>     <chr>       <chr>             <int> <chr>       <chr>             <int>
    #> 1 H      ""        HALLMARK_A… Abca1             11303 ENSMUSG000… ABCA1                19
    #> 2 H      ""        HALLMARK_A… Abcb8             74610 ENSMUSG000… ABCB8             11194
    #> 3 H      ""        HALLMARK_A… Acaa2             52538 ENSMUSG000… ACAA2             10449
    #> 4 H      ""        HALLMARK_A… Acadl             11363 ENSMUSG000… ACADL                33
    #> 5 H      ""        HALLMARK_A… Acadm             11364 ENSMUSG000… ACADM                34
    #> 6 H      ""        HALLMARK_A… Acads             11409 ENSMUSG000… ACADS                35
    #> # … with 10 more variables: 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>

## Pathway enrichment analysis

The `msigdbr` output can be used with various popular 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 (released January 2022). The MSigDB version is used as the base of the `msigdsbr` 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 other resources that and provide some of the functionality and served as an inspiration for this package. Ge Lab Gene Set Files has GMT files for many species. WEHI provides MSigDB gene sets in R format for human and mouse. MSigDF is based on the WEHI resource, but is converted to a more tidyverse-friendly data frame. These are updated at varying frequencies and may not use the latest version of MSigDB.

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")`.