This data package contains the Molecular Signature Database (MSigDB) for both human and predicted mouse orthologs in separate data frames (tibbles).
Each data frame (msigdf.human and msigdf.mouse) contain three columns: the collection (Hallmark, or c1-c8), the gene set, and Entrez IDs
for genes in that set. The msigdf.urls tibble contains links to descriptions on the Broad Institute’s website of each gene set.
Source code available on GitHub.
msigdf 2025.1
Original data from the Broad Institute’s Molecular Signature Database (MSigDB)1 http://www.broad.mit.edu/gsea/msigdb/index.jsp, redistributed as separate gmt data files from the MSigDB.
The gene sets contained in the MSigDB are from a wide variety of sources, and relate to a variety of species, mostly human. To facilitate use of the MSigDB in our work, we have created a pure mouse version of the MSigDB by mapping all sets to mouse orthologs. A pure human version is also provided.
Procedure:
1. The current MSigDB v2025.1 gmt files were downloaded from Broad ftp.
2. This was done with the human and mouse gene sets
3. Each collection was converted to a list in R, and written to a RData file using save().
See the script in data-raw/ to see how the data frames (tibbles) were created.
There are three data frames (tibbles) this package. The msigdf.human data frame has columns for each MSigDB collection divided by sub-collection (like cc, bp and mf for C5). The format of the data is tidy, so each row is a single gene set collection, sub-collection and gene symbol. The msigdf.mouse data frame has the same structure for mouse orthologs. The msigdf.urls data frame links the name of the gene set to the URL on the Broad’s website.
New C5 ontology information was added to the category subcode for easy filtering and consistency.
The data sets in this package have several million rows. The package imports the tibble package so they’re displayed nicely.
Take a look:
## # A tibble: 6 × 4
## category_code category_subcode geneset symbol
## <chr> <chr> <chr> <chr>
## 1 c1 all MT MT-ATP6
## 2 c1 all MT MT-ATP8
## 3 c1 all MT MT-CO1
## 4 c1 all MT MT-CO2
## 5 c1 all MT MT-CO3
## 6 c1 all MT MT-CYB## # A tibble: 6 × 4
## category_code category_subcode geneset symbol
## <chr> <chr> <chr> <chr>
## 1 m1 all MT mt-Atp6
## 2 m1 all MT mt-Atp8
## 3 m1 all MT mt-Co1
## 4 m1 all MT mt-Co2
## 5 m1 all MT mt-Co3
## 6 m1 all MT mt-Cytb## category_code category_subcode geneset
## 1 c1 all MT
## 2 c1 all chr10p11
## 3 c1 all chr10p12
## 4 c1 all chr10p13
## 5 c1 all chr10p14
## 6 c1 all chr10p15
## url
## 1 http://software.broadinstitute.org/gsea/msigdb/cards/MT
## 2 http://software.broadinstitute.org/gsea/msigdb/cards/chr10p11
## 3 http://software.broadinstitute.org/gsea/msigdb/cards/chr10p12
## 4 http://software.broadinstitute.org/gsea/msigdb/cards/chr10p13
## 5 http://software.broadinstitute.org/gsea/msigdb/cards/chr10p14
## 6 http://software.broadinstitute.org/gsea/msigdb/cards/chr10p15Just get the entries for the KEGG non-homologous end joining pathway:
## # A tibble: 13 × 4
## category_code category_subcode geneset symbol
## <chr> <chr> <chr> <chr>
## 1 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING DCLRE1C
## 2 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING DNTT
## 3 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING FEN1
## 4 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING LIG4
## 5 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING MRE11
## 6 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING NHEJ1
## 7 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING POLL
## 8 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING POLM
## 9 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING PRKDC
## 10 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING RAD50
## 11 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING XRCC4
## 12 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING XRCC5
## 13 c2 cp.kegg_legacy KEGG_NON_HOMOLOGOUS_END_JOINING XRCC6Some software, e.g., fGSEA might require gene sets to be a named list of genes identifiers, where the name of each element in the list is the name of the pathway. This is how the data was originally structured, and we can return to it with plyr::dlply(). Here, let’s use only the hallmark sets, and after we dlply the data into this named list format, get just the first few pathways, and in each of those, just display the first few gene symbols.
msigdf.human %>%
filter(category_code=="c2") %>%
select(geneset, symbol) %>%
group_by(geneset) %>%
summarize(symbol=list(symbol)) %>%
deframe() %>%
head() %>%
map(head)## $ABBUD_LIF_SIGNALING_1_DN
## [1] "AHNAK" "ALCAM" "ANKRD40" "ARID1A" "BCKDHB" "C16orf89"
##
## $ABBUD_LIF_SIGNALING_1_UP
## [1] "ACAA2" "ALDOC" "ANXA8L1" "BCL3" "CEBPB" "CXCL14"
##
## $ABBUD_LIF_SIGNALING_2_DN
## [1] "CGA" "CITED2" "NALCN" "PITX2" "PTHLH" "SCN1A"
##
## $ABBUD_LIF_SIGNALING_2_UP
## [1] "ATP1B1" "COL11A1" "DAB2" "DCN" "DIO2" "EZR"
##
## $ABDELMOHSEN_ELAVL4_TARGETS
## [1] "BCL2" "CAB39" "CASP3" "CDC42" "CDH2" "DLG4"
##
## $ABDULRAHMAN_KIDNEY_CANCER_VHL_DN
## [1] "ACTA2" "ALDH1A1" "ALDH3B1" "ITGB3BP" "MPPE1" "MTMR3"The number of gene sets in each collection for each organism is dependent of the construction at MSigDB.
Human Collection of gene sets https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp
## # A tibble: 26 × 3
## # Groups: category_code [9]
## category_code category_subcode n
## <chr> <chr> <int>
## 1 c1 all 42654
## 2 c2 cgp 393284
## 3 c2 cp.biocarta 4814
## 4 c2 cp.kegg_legacy 12795
## 5 c2 cp.kegg_medicus 9662
## 6 c2 cp.pid 8054
## 7 c2 cp.reactome 97590
## 8 c2 cp.wikipathways 37188
## 9 c3 mir 406258
## 10 c3 mir.mir_legacy 34178
## # ℹ 16 more rowsMouse Collection of gene sets https://www.gsea-msigdb.org/gsea/msigdb/mouse/collections.jsp
## # A tibble: 13 × 3
## # Groups: category_code [6]
## category_code category_subcode n
## <chr> <chr> <int>
## 1 m1 all 41569
## 2 m2 cgp 113138
## 3 m2 cp.biocarta 3958
## 4 m2 cp.reactome 71506
## 5 m2 cp.wikipathways 9617
## 6 m3 gtrd 163607
## 7 m3 mirdb 233394
## 8 m5 go.bp 649092
## 9 m5 go.cc 101934
## 10 m5 go.mf 109239
## 11 m5 mpt 2606
## 12 m8 all 47984
## 13 mh all 7191Get the URL for the hallmark set with the fewest number of genes (Notch signaling). Optionally, %>% this to browseURL to open it up in your browser.
msigdf.human %>%
filter(category_code=="h") %>%
count(geneset) %>%
arrange(n) %>%
head(1) %>%
inner_join(msigdf.urls, by="geneset") %>%
pull(url)## [1] "http://software.broadinstitute.org/gsea/msigdb/cards/HALLMARK_NOTCH_SIGNALING"Just look at the number of genes in each KEGG pathway (sorted descending by the number of genes in that pathway):
msigdf.human %>%
filter(category_code=="c2" & grepl("^KEGG_", geneset)) %>%
count(geneset) %>%
arrange(desc(n))## # A tibble: 844 × 2
## geneset n
## <chr> <int>
## 1 KEGG_OLFACTORY_TRANSDUCTION 389
## 2 KEGG_PATHWAYS_IN_CANCER 325
## 3 KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION 272
## 4 KEGG_MAPK_SIGNALING_PATHWAY 267
## 5 KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION 264
## 6 KEGG_REGULATION_OF_ACTIN_CYTOSKELETON 213
## 7 KEGG_FOCAL_ADHESION 199
## 8 KEGG_CHEMOKINE_SIGNALING_PATHWAY 188
## 9 KEGG_HUNTINGTONS_DISEASE 182
## 10 KEGG_ENDOCYTOSIS 181
## # ℹ 834 more rows## R version 4.5.0 (2025-04-11)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/London
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] msigdf_2025.1 lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1
## [5] dplyr_1.1.4 purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
## [9] tibble_3.3.0 ggplot2_3.5.2 tidyverse_2.0.0 knitr_1.50
## [13] BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.5.0
## [4] BiocManager_1.30.26 tidyselect_1.2.1 jquerylib_0.1.4
## [7] scales_1.4.0 yaml_2.3.10 fastmap_1.2.0
## [10] R6_2.6.1 generics_0.1.4 bookdown_0.43
## [13] tzdb_0.5.0 bslib_0.9.0 pillar_1.10.2
## [16] RColorBrewer_1.1-3 rlang_1.1.6 utf8_1.2.6
## [19] stringi_1.8.7 cachem_1.1.0 xfun_0.52
## [22] sass_0.4.10 timechange_0.3.0 cli_3.6.5
## [25] withr_3.0.2 magrittr_2.0.3 digest_0.6.37
## [28] grid_4.5.0 rstudioapi_0.17.1 hms_1.1.3
## [31] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.3
## [34] glue_1.8.0 farver_2.1.2 rmarkdown_2.29
## [37] tools_4.5.0 pkgconfig_2.0.3 htmltools_0.5.8.1