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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 x 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_… AAACCA… Abcc4 239273 ENSMUSG000… ABCC4 10257
#> 2 C3 MIR:MIR_… AAACCA… Abraxas2 109359 ENSMUSG000… ABRAXAS2 23172
#> 3 C3 MIR:MIR_… AAACCA… Actn4 60595 ENSMUSG000… ACTN4 81
#> 4 C3 MIR:MIR_… AAACCA… Acvr1 11477 ENSMUSG000… ACVR1 90
#> 5 C3 MIR:MIR_… AAACCA… Adam9 11502 ENSMUSG000… ADAM9 8754
#> 6 C3 MIR:MIR_… 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 x 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, domest…
#> 3 Caenorhabditis elegans roundworm
#> 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 972… <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 x 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 "" HALLMA… Abca1 11303 ENSMUSG000… ABCA1 19
#> 2 H "" HALLMA… Abcb8 74610 ENSMUSG000… ABCB8 11194
#> 3 H "" HALLMA… Acaa2 52538 ENSMUSG000… ACAA2 10449
#> 4 H "" HALLMA… Acadl 11363 ENSMUSG000… ACADL 33
#> 5 H "" HALLMA… Acadm 11364 ENSMUSG000… ACADM 34
#> 6 H "" HALLMA… 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 x 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_… Ahnak 66395 ENSMUSG000… AHNAK 79026
#> 2 C2 CGP ABBUD_… Alcam 11658 ENSMUSG000… ALCAM 214
#> 3 C2 CGP ABBUD_… Ankrd40 71452 ENSMUSG000… ANKRD40 91369
#> 4 C2 CGP ABBUD_… Arid1a 93760 ENSMUSG000… ARID1A 8289
#> 5 C2 CGP ABBUD_… Bckdhb 12040 ENSMUSG000… BCKDHB 594
#> 6 C2 CGP ABBUD_… 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 x 3
#> gs_cat gs_subcat num_genesets
#> <chr> <chr> <int>
#> 1 C1 "" 278
#> 2 C2 "CGP" 3368
#> 3 C2 "CP" 29
#> 4 C2 "CP:BIOCARTA" 292
#> 5 C2 "CP:KEGG" 186
#> 6 C2 "CP:PID" 196
#> 7 C2 "CP:REACTOME" 1604
#> 8 C2 "CP:WIKIPATHWAYS" 615
#> 9 C3 "MIR:MIRDB" 2377
#> 10 C3 "MIR:MIR_Legacy" 221
#> 11 C3 "TFT:GTRD" 523
#> 12 C3 "TFT:TFT_Legacy" 610
#> 13 C4 "CGN" 427
#> 14 C4 "CM" 431
#> 15 C5 "GO:BP" 7481
#> 16 C5 "GO:CC" 996
#> 17 C5 "GO:MF" 1708
#> 18 C5 "HPO" 4813
#> 19 C6 "" 189
#> 20 C7 "IMMUNESIGDB" 4872
#> 21 C7 "VAX" 347
#> 22 C8 "" 671
#> 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 x 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 "" HALLMA… Abca1 11303 ENSMUSG000… ABCA1 19
#> 2 H "" HALLMA… Abcb8 74610 ENSMUSG000… ABCB8 11194
#> 3 H "" HALLMA… Acaa2 52538 ENSMUSG000… ACAA2 10449
#> 4 H "" HALLMA… Acadl 11363 ENSMUSG000… ACADL 33
#> 5 H "" HALLMA… Acadm 11364 ENSMUSG000… ACADM 34
#> 6 H "" HALLMA… 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 v7.4 (released April 2021). 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").
