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README.Rmd
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---
output: rmarkdown::github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r echo=FALSE, message=FALSE}
knitr::opts_chunk$set(message=FALSE, comment="#>")
devtools::load_all(".")
```
# hypeR
<img src="media/logo.png" height="100px" align="right"/>
[](https://doi.org/doi:10.18129/B9.bioc.hypeR)
[](https://bioconductor.org/checkResults/3.9/bioc-LATEST/hypeR/)
[](https://www.tidyverse.org/lifecycle/#stable)
[](https://github.com/montilab/hypeR/commits/master)
## Documentation
Please visit <https://montilab.github.io/hypeR-docs/>
You can also try out our [web-application](https://hyper-shiny.shinyapps.io/wapp/) if you prefer an interface!
## Requirements
We recommend the latest version of R (\>= 4.0.0) but **hypeR** currently requires R (\>= 3.6.0) to be installed directly from Github or Bioconductor. To install with R (\>= 3.5.0) see below. Use with R (\< 3.5.0) is not recommended.
## Installation
Install the development version of the package from Github. <span style="color:#0278ae">**[Recommended]**</span>
``` r
devtools::install_github("montilab/hypeR")
```
Or install the development version of the package from Bioconductor.
``` r
BiocManager::install("montilab/hypeR", version="devel")
```
Or install with Conda.
```bash
conda create --name hyper
source activate hyper
conda install -c r r-devtools
R
library(devtools)
devtools::install_github("montilab/hypeR")
```
Or install with previous versions of R.
```bash
git clone https://github.com/montilab/hypeR
nano hypeR/DESCRIPTION
# Change Line 8
# Depends: R (>= 3.6.0) -> Depends: R (>= 3.5.0)
R
install.packages("path/to/hypeR", repos=NULL, type="source")
```
## Usage
```{r, eval=FALSE}
library(hypeR)
```
```{r}
data(wgcna)
# Process many signatures
signatures <- wgcna[[1]]
str(signatures)
# Access to hundreds of genesets
genesets <- msigdb_gsets("Homo sapiens", "C2", "CP:KEGG", clean=TRUE)
print(genesets)
```
```{r, message=FALSE, warning=FALSE, results='hide'}
mhyp <- hypeR(signatures, genesets, test="hypergeometric", background=30000)
```
```{r, fig.width=8, fig.align='center'}
hyp_dots(mhyp, merge=TRUE, fdr=0.05, title="Co-expression Modules")
```
## Terminology
### Signature
__hypeR__ employs multiple types of enrichment analyses (e.g. hypergeometric, kstest, gsea). Depending on the type, different kinds of signatures are expected. There are three types of signatures `hypeR()` expects.
```{r}
# Simply a character vector of symbols (hypergeometric)
signature <- c("GENE1", "GENE2", "GENE3")
# A ranked character vector of symbols (kstest)
ranked.signature <- c("GENE2", "GENE1", "GENE3")
# A ranked named numerical vector of symbols with ranking weights (gsea)
weighted.signature <- c("GENE2"=1.22, "GENE1"=0.94, "GENE3"=0.77)
```
### Geneset
A geneset is simply a list of vectors, therefore, one can use any custom geneset in their analyses, as long as it's appropriately defined.
```{r}
genesets <- list("GSET1" = c("GENE1", "GENE2", "GENE3"),
"GSET2" = c("GENE4", "GENE5", "GENE6"),
"GSET3" = c("GENE7", "GENE8", "GENE9"))
```
#### Hyper enrichment
All workflows begin with performing hyper enrichment with `hyper()`. Often we are just interested in a single signature, as described above. In this case, `hyper()` will return a `hyp` object. This object contains relevant information to the enrichment results and is recognized by downstream methods.
```{r}
hyp_obj <- hypeR(signature, genesets)
```
#### Downstream methods
Please visit the [documentation](https://montilab.github.io/hypeR-docs/) for detailed functionality. Below is a brief list of some methods.
##### Downloading genesets
```{r, eval=FALSE}
BIOCARTA <- msigdb_gsets(species="Homo sapiens", category="C2", subcategory="CP:BIOCARTA")
KEGG <- msigdb_gsets(species="Homo sapiens", category="C2", subcategory="CP:KEGG")
REACTOME <- msigdb_gsets(species="Homo sapiens", category="C2", subcategory="CP:REACTOME")
```
##### Visualize results
```{r, eval=FALSE}
# Show interactive table
hyp_show(hyp_obj)
# Plot dots plot
hyp_dots(hyp_obj)
# Plot enrichment map
hyp_emap(hyp_obj)
# Plot hiearchy map (relational genesets)
hyp_hmap(hyp_obj)
```
##### Saving results
```{r, eval=FALSE}
# Map enrichment to an igraph object (relational genesets)
hyp_to_graph(hyp_obj)
# Save to excel
hyp_to_excel(hyp_obj, file_path="hypeR.xlsx")
# Save to table
hyp_to_table(hyp_obj, file_path="hypeR.txt")
# Generate markdown report
hyp_to_rmd(hyp_obj,
file_path="hypeR.rmd",
title="Hyper Enrichment (hypeR)",
subtitle="YAP, TNF, and TAZ Knockout Experiments",
author="Anthony Federico, Stefano Monti")
```
## Related Repositories
- [hypeR-db](https://github.com/montilab/hypeR-db)
*A repository for commonly used open source genesets used by hypeR*
- [hypeR-shiny](https://github.com/montilab/hypeR-shiny)
*Our Shiny web application built on hypeR*
- [hypeR-modules](https://github.com/montilab/hypeR-modules)
*Integration of hypeR modules in custom Shiny applications*
- [hypeR-docs](https://github.com/montilab/hypeR-docs)
*Landing site for hosting documentation for hypeR*
- [hypeR-workshop](https://github.com/montilab/hypeR-workshop)
*Materials for a hypeR tutorial workshop*
## Cite
```{r}
citation("hypeR")
```