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Gene-set enrichment via enrichR

Source: R/enrichR_list.R
enrichR_list.Rd

Enrichment analysis of multiple gene sets against databases from enrichR (e.g. DSigDB for drug signatures, ChEA for TF targets, KEGG for pathways, DrugBank for drug targets).

Usage

enrichR_list(
  gene_list,
  databases = c("DSigDB", "DrugMatrix"),
  site = "Enrichr",
  universe = NULL,
  universe_list = NULL,
  pvalueCutoff = 0.05,
  include_overlap = FALSE
)

Arguments

gene_list

A named list of gene vectors, or a data.frame with Feature and Module columns from WGCNA_module(). Gene identifiers must match the convention of the chosen site (e.g. human gene symbols for Enrichr, fly symbols for FlyEnrichr).

databases

Character vector of enrichR database names to query. Available databases can be checked with enrichR::listEnrichrDbs(). (default: c("DSigDB", "DrugMatrix"))

site

enrichR site to query. See enrichR::listEnrichrSites(). (default: "Enrichr")

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.

pvalueCutoff

Adjusted p-value cutoff for filtering enriched terms (default: 0.05)

include_overlap

Parameter passed to enrichR::enrichr(). If TRUE, databases are downloaded during each query to output 'Overlap' when analysing with a background. (default: FALSE)

Value

A named list of data.frames, one per database. Each data.frame has columns Cluster, Description, p.adjust (Adjusted.P.value from enrichR output), Combined.Score, Genes, and any additional columns returned by the enrichR API. Compatible with plot_modules_h(enrich_list = result, enrich_category = "DSigDB").

Details

Multiple testing: P-values are adjusted (Benjamini–Hochberg) independently for each module within each database. No correction is applied across databases or across modules.

Requires an internet connection.

Examples

data(example_net)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:AnnotationDbi':
#> 
#>     select
#> The following objects are masked from 'package:IRanges':
#> 
#>     collapse, desc, intersect, setdiff, slice, union
#> The following objects are masked from 'package:S4Vectors':
#> 
#>     first, intersect, rename, setdiff, setequal, union
#> The following object is masked from 'package:Biobase':
#> 
#>     combine
#> The following objects are masked from 'package:BiocGenerics':
#> 
#>     combine, intersect, setdiff, setequal, union
#> The following object is masked from 'package:generics':
#> 
#>     explain
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
example_module <- WGCNA_module(example_net) |>
    dplyr::filter(Module %in% c("1", "2"))
# Use high pvalueCutoff for demonstration
# enrichr_out <- enrichR_list(example_module,
#     databases = c("KEGG_2019_Mouse"),
#     universe = WGCNA_module(example_net, exclude_grey = FALSE)$Feature,
#     pvalueCutoff = 0.5)