README.Rmd

---
title: "An R interface to the Enrichr database"
author: "Wajid Jawaid"
email: "wj241@alumni.cam.ac.uk"
date: "`r Sys.Date()`"
<!-- bibliography: ./vignettes/enrichr.bib -->
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "./tools/README-"
)
```

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/enrichR)](https://cran.r-project.org/package=enrichR)
[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![CRAN mirror downloads](https://cranlogs.r-pkg.org/badges/enrichR)](https://cran.r-project.org/package=enrichR/)

# Installation

**enrichR** can be installed from Github or from CRAN.

## Github

```{r, echo = TRUE, eval = FALSE}
library(devtools)
install_github("wjawaid/enrichR")
```

## CRAN

The package can be downloaded from CRAN using:

```{r, echo = TRUE, eval = FALSE}
install.packages("enrichR")
```

# Usage example

**enrichR** provides an interface to the Enrichr database [@kuleshov_enrichr:_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)

```{r, echo = TRUE, eval = TRUE}
library(enrichR)
listEnrichrSites()
setEnrichrSite("Enrichr") # Human genes
```

Then find the list of all available databases from Enrichr.

```{r, echo = TRUE, eval = TRUE}
dbs <- listEnrichrDbs()
```

```{r, echo = TRUE, eval = FALSE}
head(dbs)
```

```{r, echo = FALSE, results='asis'}
library(knitr)
kable(head(dbs[c(1:6),-4]))
```

View and select your favourite databases. Then query enrichr, in this case I have used genes associated with embryonic haematopoiesis.

```{r, echo = TRUE, eval = TRUE}
dbs <- c("GO_Molecular_Function_2015", "GO_Cellular_Component_2015", "GO_Biological_Process_2015")
enriched <- enrichr(c("Runx1", "Gfi1", "Gfi1b", "Spi1", "Gata1", "Kdr"), dbs)
```

Now view the results table.

```{r, echo = TRUE, eval = FALSE}
enriched[["GO_Biological_Process_2015"]]
```

You can give many genes.
```{r, echo = TRUE, eval = FALSE}
data(genes790)
length(genes790)
head(enrichr(genes790, c('LINCS_L1000_Chem_Pert_up'))[[1]])
```

```{r, echo = FALSE, results = 'asis'}
x <- head(enriched[["GO_Biological_Process_2015"]])
x[,1] <- gsub("GO:", "GO_", x[,1])
kable(x)
```

Plot Enrichr GO-BP output. (Plotting function contributed by I-Hsuan Lin)

```{r, echo = TRUE, eval = TRUE, fig.width = 8, fig.height = 6, fig.align = "center", dpi = 100}
plotEnrich(enriched[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
```

# 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–W97. https://doi.org/10.1093/nar/gkw377.