GO enrichment with gene sets — enrichGO

Gene ontology enrichment analysis of multiple gene sets with clusterProfiler, allowing for using different or same background genes for each gene set

Usage

enrichGO_list(
  gene_list,
  keyType = "SYMBOL",
  OrgDb,
  universe = NULL,
  universe_list = NULL,
  pAdjustMethod = "BH",
  pvalueCutoff = 0.05,
  category = NULL,
  simplify = FALSE,
  simplify_cutoff = 0.7,
  simplify_by = "p.adjust",
  simplify_select_fun = min,
  simplify_measure = "Wang",
  ...
)

Arguments

gene_list: A list of gene sets (vectors)
keyType: (Optional) Available options are AnnotationDbi::keytypes(OrgDb) (default is "SYMBOL")
OrgDb: Organism database, e.g. org.Hs.eg.db, org.Mm.eg.db
universe: Background genes for all input gene sets, used if universe_list is not provided
universe_list: Background genes for each input gene set, a list of gene vectors with the same names as gene_list
pAdjustMethod: (Optional) Parameter of clusterProfiler::enrichGO() (default is "BH")
pvalueCutoff: (Optional) Parameter of clusterProfiler::enrichGO() (default is 0.05)
category: (Optional) GO category to analyze (default is all three of BP, MF, CC)
simplify: (Optional) Whether to simplify the GO terms by removing redundant terms with clusterProfiler::simplify() function. (default is FALSE)
simplify_cutoff: (Optional) Parameter of clusterProfiler::simplify(), cutoff for similarity when simplifying GO terms (default is 0.7)
simplify_by: (Optional) Parameter of clusterProfiler::simplify(), method to choose representative term when simplifying GO terms (default is "p.adjust")
simplify_select_fun: (Optional) Parameter of clusterProfiler::simplify(), function to select representative term when simplifying GO terms (default is min)
simplify_measure: (Optional) Parameter of clusterProfiler::simplify(), method to calculate similarity when simplifying GO terms (default is "Wang")
...: additional arguments passed to clusterProfiler::enrichGO()

Value

A nested list of GO enrichment results with sublists: 'all' including all terms and 'simplified' including simplified terms (if simplify = TRUE), both containing further sublists for each GO category (BP, MF, CC).

Examples

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(org.Mm.eg.db)
#> Loading required package: AnnotationDbi
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: generics
#> 
#> Attaching package: 'generics'
#> The following object is masked from 'package:dplyr':
#> 
#>     explain
#> The following objects are masked from 'package:base':
#> 
#>     as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
#>     setequal, union
#> 
#> Attaching package: 'BiocGenerics'
#> The following object is masked from 'package:dplyr':
#> 
#>     combine
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
#>     as.data.frame, basename, cbind, colnames, dirname, do.call,
#>     duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply,
#>     mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
#>     rank, rbind, rownames, sapply, saveRDS, table, tapply, unique,
#>     unsplit, which.max, which.min
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> Loading required package: IRanges
#> Loading required package: S4Vectors
#> 
#> Attaching package: 'S4Vectors'
#> The following objects are masked from 'package:dplyr':
#> 
#>     first, rename
#> The following object is masked from 'package:utils':
#> 
#>     findMatches
#> The following objects are masked from 'package:base':
#> 
#>     I, expand.grid, unname
#> 
#> Attaching package: 'IRanges'
#> The following objects are masked from 'package:dplyr':
#> 
#>     collapse, desc, slice
#> The following object is masked from 'package:grDevices':
#> 
#>     windows
#> 
#> Attaching package: 'AnnotationDbi'
#> The following object is masked from 'package:dplyr':
#> 
#>     select
#> 
library(clusterProfiler)
#> 
#> clusterProfiler v4.21.0 Learn more at https://yulab-smu.top/contribution-knowledge-mining/
#> 
#> Please cite:
#> 
#> S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang,
#> W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize
#> multiomics data. Nature Protocols. 2024, 19(11):3292-3320
#> 
#> Attaching package: 'clusterProfiler'
#> The following object is masked from 'package:AnnotationDbi':
#> 
#>     select
#> The following object is masked from 'package:IRanges':
#> 
#>     slice
#> The following object is masked from 'package:S4Vectors':
#> 
#>     rename
#> The following object is masked from 'package:stats':
#> 
#>     filter
data(example_net)
example_module <- data.frame(Module = as.factor(example_net$colors)) %>%
    tibble::rownames_to_column("Feature") %>% arrange(Module)
# select two modules for demonstration
example_module_list <- example_module %>% 
    filter(Module %in% c(1, 2)) %>%
    split(as.character(.$Module)) %>%
    lapply(`[[`, "Feature")
# set cutoff to 1 to show all results for demonstration
example_go_list = enrichGO_list(example_module_list, OrgDb = org.Mm.eg.db,
    universe = example_module$Feature,
    pvalueCutoff = 1, qvalueCutoff = 1,
    category = "BP", simplify = FALSE)
#> Performing GO enrichment for category: BP
#> Processing gene list: 1
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning: 1.03% of input gene IDs are fail to map...
#> Processing gene list: 2
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning: 5.88% of input gene IDs are fail to map...
#> 'select()' returned 1:1 mapping between keys and columns
#> Warning: 1.03% of input gene IDs are fail to map...
#> Merging GO enrichment results across gene lists for each category.
# plot_GO(example_go_list$all, plot_dotplot = TRUE,
#     plot_emapplot = FALSE, plot_cnetplot = FALSE)