Enrichr

https://github.com/wjawaid/enrichr
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An R interface to the Enrichr database, allowing users to perform gene-set enrichment analysis with...
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### Install enrichR from GitHub

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Installs the enrichR package directly from its GitHub repository using the devtools package. This is useful for obtaining the latest development version.

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

--------------------------------

### Install enrichR from CRAN

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Installs the enrichR package from the Comprehensive R Archive Network (CRAN). This is the standard method for installing stable releases of R packages.

```r
install.packages("enrichR")
```

--------------------------------

### Perform Enrichment Analysis (Without Background)

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Performs gene-set enrichment analysis using the enrichr() function with a list of input genes and selected databases. It shows how to load example genes and view the results.

```r
# Load example input genes
data(input)

# Perform enrichment analysis
enriched <- enrichr(input, dbs)

# View results for a specific database
head(enriched["GO_Biological_Process_2023"])
```

--------------------------------

### List and Select Gene-Set Libraries

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Demonstrates how to list all available gene-set libraries from the Enrichr database and how to select specific libraries for analysis, such as GO databases from 2023.

```r
# List all available databases
dbs <- listEnrichrDbs()

# Select specific GO databases for analysis
dbs <- c("GO_Molecular_Function_2023", "GO_Cellular_Component_2023",
         "GO_Biological_Process_2023")
```

--------------------------------

### Initialize Enrichr Connection

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Loads the enrichR library and checks the connection status to various Enrichr-related services. It also demonstrates how to set the specific Enrichr site to use for analysis, defaulting to the human gene set.

```r
library(enrichR)

listEnrichrSites()

setEnrichrSite("Enrichr") # Sets connection to the default human Enrichr site
```

--------------------------------

### Load and Inspect Background Data

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Loads a predefined background dataset into the R environment and displays its total length and the first few elements. This step is crucial for understanding the scope of the background set used in enrichment analysis.

```R
data(background)
length(background)
#\> [1] 20625
head(background)
#\> [1] "A1BG"     "A2M"      "NAT1"     "NAT2"     "SERPINA3" "AADAC"
```

--------------------------------

### Configure Proxy Settings for Enrichr

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Sets R options to configure HTTP or HTTPS proxy settings for the Enrichr package. This is necessary when the R environment is behind a proxy server, ensuring successful connection to the Enrichr database.

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

--------------------------------

### View Enrichment Results (Default)

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Displays the top results for a specific enrichment database, 'GO_Biological_Process_2023', from the 'enriched2' object. This shows terms, ranks, p-values, and associated genes, providing insights into biological pathways.

```R
head(enriched2[["GO_Biological_Process_2023"]])
```

--------------------------------

### Perform Enrichment with Background Set

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Conducts gene enrichment analysis using the enrichr function. It takes a list of genes ('input') and a set of databases ('dbs'), specifying a 'background' gene list for comparative analysis. The function uploads data and retrieves enrichment results from specified databases.

```R
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.
```

--------------------------------

### Export Enrichment Results to Excel

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Exports the enrichment results into a single Excel 2007 (XLSX) file, with each worksheet corresponding to a selected database. This provides a convenient way to manage and share multiple result sets.

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

--------------------------------

### Visualize Enrichment Results

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Generates a plot to visualize the enrichment results for a specified database ('GO_Biological_Process_2023'). The plot displays the top terms, ordered by p-value, showing gene counts and other relevant metrics, with customizable display options.

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

--------------------------------

### Export Enrichment Results to Text Files

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Exports the enrichment results from all selected databases into individual text files. This function is useful for saving detailed results for further analysis or reporting.

```R
# To text files
printEnrich(enriched)
```

--------------------------------

### View Enrichment Results with Overlap

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Displays the top results for 'GO_Biological_Process_2023' from the 'enriched3' object, now including the 'Overlap' column. This column shows the ratio of overlapping genes for each term, aiding in the assessment of enrichment significance.

```R
head(enriched3[["GO_Biological_Process_2023"]])
```

--------------------------------

### Perform Enrichment with Overlap Calculation

Source: https://github.com/wjawaid/enrichr/blob/master/README.md

Executes gene enrichment analysis and includes the 'Overlap' column in the results by setting 'include_overlap = TRUE'. This option calculates the number of annotated genes within each term relative to the background set, enhancing result interpretation.

```R
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.
```
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