Last updated: 2023-12-13
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Knit directory: muse/
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| Rmd | 5b0e194 | Dave Tang | 2023-12-13 | Downloading molecular signatures in R |
Following the vignette.
The molecular signatures database (MSigDB) is one of the largest collections of molecular signatures or gene expression signatures. A variety of gene expression signatures are hosted on this database including experimentally derived signatures and signatures representing pathways and ontologies from other curated databases. This rich collection of gene expression signatures (>25,000) can facilitate a wide variety of signature-based analyses, the most popular being gene set enrichment analyses. These signatures can be used to perform enrichment analysis in a DE experiment using tools such as {GSEA}, {fry} (from {limma}) and {camera} (from {limma}). Alternatively, they can be used to perform single-sample gene-set analysis of individual transcriptomic profiles using approaches such as {singscore}, {ssGSEA} and {GSVA}.
This package provides the gene sets in the MSigDB in the form of
GeneSetobjects. This data structure is specifically designed to store information about gene sets, including their member genes and metadata. Other packages, such as {msigdbr} and {EGSEAdata} provide these gene sets too, however, they do so by storing them as lists or tibbles. These structures are not specific to gene sets therefore do not allow storage of important metadata associated with each gene set, for example, their short and long descriptions. Additionally, the lack of structure allows creation of invalid gene sets. Accessory functions implemented in the {GSEABase} package provide a neat interface to interact withGeneSetobjects.
Install GSVA. (Dependencies are listed in the Imports section in the DESCRIPTION file.)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("msigdb", quietly = TRUE))
BiocManager::install("msigdb")Load package.
library(msigdb)
packageVersion("msigdb")[1] '1.10.0'In order to download the MSigDB database, we need to load {ExperimentHub} and {GSEABase}.
library(ExperimentHub)
library(GSEABase)Query an ExperimentHub object.
eh <- ExperimentHub(ask = FALSE)
AnnotationHub::query(x = eh, pattern = 'msigdb')ExperimentHub with 49 records
# snapshotDate(): 2023-10-24
# $dataprovider: Broad Institute, Emory University, EBI
# $species: Homo sapiens, Mus musculus
# $rdataclass: GSEABase::GeneSetCollection, list, data.frame
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["EH5421"]]'
title
EH5421 | msigdb.v7.2.hs.SYM
EH5422 | msigdb.v7.2.hs.EZID
EH5423 | msigdb.v7.2.mm.SYM
EH5424 | msigdb.v7.2.mm.EZID
EH6727 | MSigDB C8 MANNO MIDBRAIN
... ...
EH8296 | msigdb.v7.5.1.hs.SYM
EH8297 | msigdb.v7.5.1.mm.EZID
EH8298 | msigdb.v7.5.1.mm.idf
EH8299 | msigdb.v7.5.1.mm.SYM
EH8300 | imex_hsmm_0722 Specify a more specific pattern to look for only human collections.
AnnotationHub::query(x = eh, pattern = 'msigdb.*hs.SYM')ExperimentHub with 7 records
# snapshotDate(): 2023-10-24
# $dataprovider: Broad Institute
# $species: Homo sapiens
# $rdataclass: GSEABase::GeneSetCollection
# additional mcols(): taxonomyid, genome, description,
# coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
# rdatapath, sourceurl, sourcetype
# retrieve records with, e.g., 'object[["EH5421"]]'
title
EH5421 | msigdb.v7.2.hs.SYM
EH6772 | msigdb.v7.3.hs.SYM
EH6778 | msigdb.v7.4.hs.SYM
EH7359 | msigdb.v7.5.hs.SYM
EH8284 | msigdb.v2022.1.hs.SYM
EH8290 | msigdb.v2023.1.hs.SYM
EH8296 | msigdb.v7.5.1.hs.SYM The experiment hubs seem to be ordered from earliest to latest.
AnnotationHub::query(x = eh, pattern = 'msigdb.*hs.SYM') |>
tail(1) -> msigdb_hs_latest
names(msigdb_hs_latest)[1] "EH8296"msigdb_hs_latestExperimentHub with 1 record
# snapshotDate(): 2023-10-24
# names(): EH8296
# package(): msigdb
# $dataprovider: Broad Institute
# $species: Homo sapiens
# $rdataclass: GSEABase::GeneSetCollection
# $rdatadateadded: 2023-07-03
# $title: msigdb.v7.5.1.hs.SYM
# $description: Gene expression signatures (Homo sapiens) from the Molecular...
# $taxonomyid: 9606
# $genome: NA
# $sourcetype: XML
# $sourceurl: https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.5...
# $sourcesize: NA
# $tags: c("Homo_sapiens_Data", "Mus_musculus_Data")
# retrieve record with 'object[["EH8296"]]' Data can be downloaded using the unique ID.
eh[[names(msigdb_hs_latest)]]GeneSetCollection
names: chr1p11, chr1p12, ..., GOMF_STARCH_BINDING (45226 total)
unique identifiers: RPL22P6, NBPF8, ..., POM121L15P (41072 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: BroadCollection (1 total)Data can also be downloaded using
msigdb::getMsigdb().
msigdb_ver <- sub(pattern = "msigdb.v(.*).hs.SYM", replacement = "\\1", msigdb_hs_latest$title)
msigdb.hs <- msigdb::getMsigdb(org = "hs", id = "SYM", version = msigdb_ver)see ?msigdb and browseVignettes('msigdb') for documentationloading from cachemsigdb.hsGeneSetCollection
names: chr1p11, chr1p12, ..., GOMF_STARCH_BINDING (45226 total)
unique identifiers: RPL22P6, NBPF8, ..., POM121L15P (41072 total)
types in collection:
geneIdType: SymbolIdentifier (1 total)
collectionType: BroadCollection (1 total)A GeneSetCollection object is effectively a list and
therefore all list processing functions work.
length(msigdb.hs)[1] 45226Each signature is stored in a GeneSet object and can be
processed using functions from the {GSEABase} package.
gs <- msigdb.hs[[1000]]
geneIds(gs) [1] "HOXC13" "MYC" "BTG3" "PIK3CG" "MXI1"
[6] "NCSTN" "HOXA11" "GADD45B" "RFNG" "FZD6"
[11] "CCND2" "PRKCD" "SOCS2" "TNFSF13B" "FANCA"
[16] "FOSB" "VSX2" "MBD1" "PSEN2" "HES3"
[21] "PRDM1" "POU2AF1" "XBP1" "STAT5A" "AKT1"
[26] "COMMD3-BMI1" "DTX2" "MIB1" "IRF4" "TRAF3"
[31] "IRF6" Details of a gene set.
details(gs)setName: SHIN_B_CELL_LYMPHOMA_CLUSTER_2
geneIds: HOXC13, MYC, ..., IRF6 (total: 31)
geneIdType: Symbol
collectionType: Broad
bcCategory: c2 (Curated)
bcSubCategory: CGP
setIdentifier: LVY1HGGWMJ7:35020:Fri May 26 12:20:46 2023:93736
description: Cluster 2 of genes distinguishing among different B lymphocyte neoplasms.
(longDescription available)
organism: Mus musculus
pubMedIds: 19010892
urls: https://data.broadinstitute.org/gsea-msigdb/msigdb/release/7.5.1/msigdb_v7.5.1.xml
contributor: Jessica Robertson
setVersion: 7.5.1
creationDate: table(sapply(lapply(msigdb.hs, collectionType), bcCategory))
c1 c2 c3 c4 c5 c6 c7 c8 h
299 6180 3726 858 28005 189 5219 700 50
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] GSEABase_1.64.0 graph_1.80.0 annotate_1.80.0
[4] XML_3.99-0.16 AnnotationDbi_1.64.1 IRanges_2.36.0
[7] S4Vectors_0.40.2 Biobase_2.62.0 ExperimentHub_2.10.0
[10] AnnotationHub_3.10.0 BiocFileCache_2.10.1 dbplyr_2.4.0
[13] BiocGenerics_0.48.1 msigdb_1.10.0 BiocManager_1.30.22
[16] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 dplyr_1.1.4
[3] blob_1.2.4 filelock_1.0.3
[5] Biostrings_2.70.1 bitops_1.0-7
[7] fastmap_1.1.1 RCurl_1.98-1.13
[9] promises_1.2.1 digest_0.6.33
[11] mime_0.12 lifecycle_1.0.4
[13] ellipsis_0.3.2 processx_3.8.3
[15] KEGGREST_1.42.0 interactiveDisplayBase_1.40.0
[17] RSQLite_2.3.4 magrittr_2.0.3
[19] compiler_4.3.2 rlang_1.1.2
[21] sass_0.4.8 tools_4.3.2
[23] utf8_1.2.4 yaml_2.3.8
[25] knitr_1.45 bit_4.0.5
[27] curl_5.2.0 withr_2.5.2
[29] purrr_1.0.2 fansi_1.0.6
[31] git2r_0.33.0 xtable_1.8-4
[33] cli_3.6.2 rmarkdown_2.25
[35] crayon_1.5.2 generics_0.1.3
[37] rstudioapi_0.15.0 httr_1.4.7
[39] DBI_1.1.3 cachem_1.0.8
[41] stringr_1.5.1 zlibbioc_1.48.0
[43] XVector_0.42.0 vctrs_0.6.5
[45] jsonlite_1.8.8 callr_3.7.3
[47] bit64_4.0.5 jquerylib_0.1.4
[49] glue_1.6.2 ps_1.7.5
[51] stringi_1.8.3 BiocVersion_3.18.1
[53] later_1.3.2 GenomeInfoDb_1.38.1
[55] tibble_3.2.1 pillar_1.9.0
[57] rappdirs_0.3.3 htmltools_0.5.7
[59] GenomeInfoDbData_1.2.11 R6_2.5.1
[61] rprojroot_2.0.4 evaluate_0.23
[63] shiny_1.8.0 png_0.1-8
[65] memoise_2.0.1 httpuv_1.6.13
[67] bslib_0.6.1 Rcpp_1.0.11
[69] whisker_0.4.1 xfun_0.41
[71] fs_1.6.3 getPass_0.2-4
[73] pkgconfig_2.0.3