An R interface to the Enrichr database

Wajid Jawaid 2025-02-02

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	Odds.Ratio	Combined.Score	Genes
Mitochondrial Transcription (GO:0006390)	3/12	0.0012685	0.7123925	17.577061	117.23788	TFAM;POLRMT;TFB1M
Alpha-Amino Acid Metabolic Process (GO:1901605)	4/29	0.0019937	0.7123925	8.452830	52.55773	SRR;ALDH6A1;KMO;GNMT
Protein Transmembrane Import Into Intracellular Organelle (GO:0044743)	4/32	0.0028882	0.7123925	7.546015	44.12249	DNAJC19;TIMM44;TRIM37;PEX1
Neutrophil Degranulation (GO:0043312)	2/5	0.0033774	0.7123925	35.070599	199.57464	VAMP8;STXBP2
Medium-Chain Fatty Acid Biosynthetic Process (GO:0051792)	2/5	0.0033774	0.7123925	35.070599	199.57464	ABHD3;OXSM
Mitochondrial RNA Metabolic Process (GO:0000959)	3/20	0.0058819	0.7123925	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	Odds.Ratio	Combined.Score	Genes
Mitochondrial Transcription (GO:0006390)	1	0.0003711	0.240515	27.116000	214.19193	TFAM;POLRMT;TFB1M
Alpha-Amino Acid Metabolic Process (GO:1901605)	2	0.0004145	0.240515	13.057671	101.69976	SRR;ALDH6A1;KMO;GNMT
Protein Transmembrane Import Into Intracellular Organelle (GO:0044743)	3	0.0006097	0.240515	11.656913	86.29136	DNAJC19;TIMM44;TRIM37;PEX1
Monocarboxylic Acid Biosynthetic Process (GO:0072330)	4	0.0012176	0.240515	6.816532	45.74506	ALDH1A3;SRR;SCP2;OXSM;MCAT
Neutrophil Degranulation (GO:0043312)	5	0.0014663	0.240515	54.031872	352.55862	VAMP8;STXBP2
Medium-Chain Fatty Acid Biosynthetic Process (GO:0051792)	6	0.0014663	0.240515	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	Odds.Ratio	Combined.Score	Genes
Mitochondrial Transcription (GO:0006390)	3/12	0.0003711	0.240515	27.116000	214.19193	TFAM;POLRMT;TFB1M
Alpha-Amino Acid Metabolic Process (GO:1901605)	4/29	0.0004145	0.240515	13.057671	101.69976	SRR;ALDH6A1;KMO;GNMT
Protein Transmembrane Import Into Intracellular Organelle (GO:0044743)	4/32	0.0006097	0.240515	11.656913	86.29136	DNAJC19;TIMM44;TRIM37;PEX1
Monocarboxylic Acid Biosynthetic Process (GO:0072330)	5/65	0.0012176	0.240515	6.816532	45.74506	ALDH1A3;SRR;SCP2;OXSM;MCAT
Neutrophil Degranulation (GO:0043312)	2/5	0.0014663	0.240515	54.031872	352.55862	VAMP8;STXBP2
Medium-Chain Fatty Acid Biosynthetic Process (GO:0051792)	2/5	0.0014663	0.240515	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.