An R interface to the Enrichr database

Wajid Jawaid 2025-02-02

CRAN_Status_Badge Project Status: Active - The project has reached a stable, usable state and is being actively developed. CRAN mirror downloads

Installation

enrichR can be installed from Github or from CRAN.

Github

library(devtools)
install_github("wjawaid/enrichR")

CRAN

The package can be downloaded from CRAN using:

install.packages("enrichR")

Usage example

enrichR provides an interface to the Enrichr database (Kuleshov et al. 2016) hosted at https://maayanlab.cloud/Enrichr/.

By default human genes are selected otherwise select your organism of choice. (This functionality was contributed by Alexander Blume)

Initialising connection to Enrichr website

library(enrichR)
#> Welcome to enrichR
#> Checking connections ...
#> Enrichr ... Connection is Live!
#> FlyEnrichr ... Connection is Live!
#> WormEnrichr ... Connection is Live!
#> YeastEnrichr ... Connection is Live!
#> FishEnrichr ... Connection is Live!
#> OxEnrichr ... Connection is Live!

listEnrichrSites()
#> Enrichr ... Connection is Live!
#> FlyEnrichr ... Connection is Live!
#> WormEnrichr ... Connection is Live!
#> YeastEnrichr ... Connection is Live!
#> FishEnrichr ... Connection is Live!
#> OxEnrichr ... Connection is Live!

setEnrichrSite("Enrichr") # Human genes
#> Connection changed to https://maayanlab.cloud/Enrichr/
#> Connection is Live!

Select gene-set libraries

List all available databases from Enrichr.

dbs <- listEnrichrDbs()
head(dbs)
geneCoverage genesPerTerm libraryName numTerms appyter categoryId
13362 275 Genome_Browser_PWMs 615 ea115789fcbf12797fd692cec6df0ab4dbc79c6a 1
27884 1284 TRANSFAC_and_JASPAR_PWMs 326 7d42eb43a64a4e3b20d721fc7148f685b53b6b30 1
6002 77 Transcription_Factor_PPIs 290 849f222220618e2599d925b6b51868cf1dab3763 1
47172 1370 ChEA_2013 353 7ebe772afb55b63b41b79dd8d06ea0fdd9fa2630 7
47107 509 Drug_Perturbations_from_GEO_2014 701 ad270a6876534b7cb063e004289dcd4d3164f342 7
21493 3713 ENCODE_TF_ChIP-seq_2014 498 497787ebc418d308045efb63b8586f10c526af51 7

Select the 2023 GO databases.

dbs <- c("GO_Molecular_Function_2023", "GO_Cellular_Component_2023",
         "GO_Biological_Process_2023")

Perform analysis

Without background

Query with enrichr() using example genes available from the package.

# Load example input genes
data(input)
length(input)
#> [1] 375
head(input)
#> [1] "Nsun3"    "Polrmt"   "Nlrx1"    "Sfxn5"    "Zc3h12c"  "Slc25a39"

enriched <- enrichr(input, dbs)
#> Uploading data to Enrichr... Done.
#>   Querying GO_Molecular_Function_2023... Done.
#>   Querying GO_Cellular_Component_2023... Done.
#>   Querying GO_Biological_Process_2023... Done.
#> Parsing results... Done.

Now view the "GO_Biological_Process_2023" results from the enriched object.

head(enriched[["GO_Biological_Process_2023"]])
Term Overlap P.value Adjusted.P.value Old.P.value Old.Adjusted.P.value Odds.Ratio Combined.Score Genes
Mitochondrial Transcription (GO:0006390) 3/12 0.0012685 0.7123925 0 0 17.577061 117.23788 TFAM;POLRMT;TFB1M
Alpha-Amino Acid Metabolic Process (GO:1901605) 4/29 0.0019937 0.7123925 0 0 8.452830 52.55773 SRR;ALDH6A1;KMO;GNMT
Protein Transmembrane Import Into Intracellular Organelle (GO:0044743) 4/32 0.0028882 0.7123925 0 0 7.546015 44.12249 DNAJC19;TIMM44;TRIM37;PEX1
Neutrophil Degranulation (GO:0043312) 2/5 0.0033774 0.7123925 0 0 35.070599 199.57464 VAMP8;STXBP2
Medium-Chain Fatty Acid Biosynthetic Process (GO:0051792) 2/5 0.0033774 0.7123925 0 0 35.070599 199.57464 ABHD3;OXSM
Mitochondrial RNA Metabolic Process (GO:0000959) 3/20 0.0058819 0.7123925 0 0 9.301708 47.77237 TFAM;POLRMT;TFB1M

With background

You can now add background genes when using enrichr().

# Load example background
data(background)
length(background)
#> [1] 20625
head(background)
#> [1] "A1BG"     "A2M"      "NAT1"     "NAT2"     "SERPINA3" "AADAC"

enriched2 <- enrichr(input, dbs, background = background)
#> Uploading data to Speedrichr...
#>  - Your gene set... Done.
#>  - Your background... Done.
#> Getting enrichment results...
#>  - GO_Molecular_Function_2023... Done.
#>  - GO_Cellular_Component_2023... Done.
#>  - GO_Biological_Process_2023... Done.
#> Parsing results... Done.

Now view the "GO_Biological_Process_2023" results from the enriched2 object.

head(enriched2[["GO_Biological_Process_2023"]])
Term Rank P.value Adjusted.P.value Old.P.value Old.Adjusted.P.value Odds.Ratio Combined.Score Genes
Mitochondrial Transcription (GO:0006390) 1 0.0003711 0.240515 0 0 27.116000 214.19193 TFAM;POLRMT;TFB1M
Alpha-Amino Acid Metabolic Process (GO:1901605) 2 0.0004145 0.240515 0 0 13.057671 101.69976 SRR;ALDH6A1;KMO;GNMT
Protein Transmembrane Import Into Intracellular Organelle (GO:0044743) 3 0.0006097 0.240515 0 0 11.656913 86.29136 DNAJC19;TIMM44;TRIM37;PEX1
Monocarboxylic Acid Biosynthetic Process (GO:0072330) 4 0.0012176 0.240515 0 0 6.816532 45.74506 ALDH1A3;SRR;SCP2;OXSM;MCAT
Neutrophil Degranulation (GO:0043312) 5 0.0014663 0.240515 0 0 54.031872 352.55862 VAMP8;STXBP2
Medium-Chain Fatty Acid Biosynthetic Process (GO:0051792) 6 0.0014663 0.240515 0 0 54.031872 352.55862 ABHD3;OXSM

By default, the results table from analysis with a background does not have the ‘Overlap’ column. We can calculate the annotated genes in each term from GMT files and replace the ‘Rank’ column with ‘Overlap’ by setting include_overlap = TRUE.

enriched3 <- enrichr(input, dbs, background = background, include_overlap = TRUE)
#> Uploading data to Speedrichr...
#>  - Your gene set... Done.
#>  - Your background... Done.
#> Getting enrichment results...
#>  - GO_Molecular_Function_2023... Done.
#>    - Download GMT file... Done.
#>  - GO_Cellular_Component_2023... Done.
#>    - Download GMT file... Done.
#>  - GO_Biological_Process_2023... Done.
#>    - Download GMT file... Done.
#> Parsing results... Done.

Now view the "GO_Biological_Process_2023" results from the enriched3 object.

head(enriched3[["GO_Biological_Process_2023"]])
Term Overlap P.value Adjusted.P.value Old.P.value Old.Adjusted.P.value Odds.Ratio Combined.Score Genes
Mitochondrial Transcription (GO:0006390) 3/12 0.0003711 0.240515 0 0 27.116000 214.19193 TFAM;POLRMT;TFB1M
Alpha-Amino Acid Metabolic Process (GO:1901605) 4/29 0.0004145 0.240515 0 0 13.057671 101.69976 SRR;ALDH6A1;KMO;GNMT
Protein Transmembrane Import Into Intracellular Organelle (GO:0044743) 4/32 0.0006097 0.240515 0 0 11.656913 86.29136 DNAJC19;TIMM44;TRIM37;PEX1
Monocarboxylic Acid Biosynthetic Process (GO:0072330) 5/65 0.0012176 0.240515 0 0 6.816532 45.74506 ALDH1A3;SRR;SCP2;OXSM;MCAT
Neutrophil Degranulation (GO:0043312) 2/5 0.0014663 0.240515 0 0 54.031872 352.55862 VAMP8;STXBP2
Medium-Chain Fatty Acid Biosynthetic Process (GO:0051792) 2/5 0.0014663 0.240515 0 0 54.031872 352.55862 ABHD3;OXSM

Visualise results

Plot the "GO_Biological_Process_2023" results. (Plotting function contributed by I-Hsuan Lin)

plotEnrich(enriched[["GO_Biological_Process_2023"]], showTerms = 20, numChar = 40, 
       y = "Count", orderBy = "P.value")

Export results

Export Enrichr results as text or Excel files. By default (i.e. outFile = "txt"), the results from all the selected databases are saved into individual text files. When using outFile = "excel", the results are saved into worksheets in a single Excel 2007 (XLSX) file. (Print function contributed by I-Hsuan Lin and Kai Hu)

# To text files
printEnrich(enriched)

# To Excel
printEnrich(enriched, outFile = "excel")

Using enrichR behind a proxy

If your computer is behind an HTTP or HTTPS proxy, you can set the RCurl Proxy options explicitly using RCurlOptions and enrichR will use the provided settings to connect to the Enrichr database via httr::use_proxy().

For example:

options(RCurlOptions = list(proxy = 'http://ip_or_url',
                            proxyusername = 'myuser',
                            proxypassword = 'mypwd',
                            proxyport = 'port_num',
                            proxyauth = 'basic'))

References

Kuleshov, Maxim V., Matthew R. Jones, Andrew D. Rouillard, Nicolas F. Fernandez, Qiaonan Duan, Zichen Wang, Simon Koplev, et al. 2016. “Enrichr: A Comprehensive Gene Set Enrichment Analysis Web Server 2016 Update.” Nucleic Acids Res 44 (Web Server issue): W90–97. https://doi.org/10.1093/nar/gkw377.