https://github.com/ccicb/easyenrich

Find elements that occur more frequently in one cohort compared to another

https://github.com/ccicb/easyenrich

Repository

Find elements that occur more frequently in one cohort compared to another

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# easyenrich


[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![CRAN status](https://www.r-pkg.org/badges/version/easyenrich)](https://CRAN.R-project.org/package=easyenrich)


Test whether counts of an element differs between 2 groups. For example identify which genes are present at different rates in lists of 'mutated genes' from two different cohorts. **easyenrich** creates 1 contingency table for every element in a list of vectors, summarising counts of their presence/absence in each cohort (represented as the different vectors in the list). It also runs the fisher test, applies MTC, and produces forest plots to visualise the results.


## Installation

You can install the development version of easyenrich like so:

``` r
if (!require("remotes"))
    install.packages("remotes")

remotes::install_github("CCICB/easyenrich")
```

## Quick Start


```{r example, fig.height=3, fig.width=7}
library(easyenrich)

#Step 1: Create a list of two or more vectors.  
input <- list(
 colorectal = c("APC", "APC", "APC", "TP53", "APC"),
 melanoma = c("BRAF", "BRAF", "BRAF", "BRAF", "BRAF", "TP53", "APC")
)

# Step 2: Create contingency tables
contingency_tables <- list_to_contingency_tables(input)

# Step 3: Compute fisher p values & odds ratios
# Odds ratios can be interpreted as the relative odds of seeing gene  in a colorectal cancer VS melanoma (based on order of cohorts in original input list)
comparison <- contingency_tables_to_fisher(contingency_tables)

# Step 4: Sort results based on fdr and print result
comparison[order(comparison$fdr),]

# Step 5: Visualise Results
plot_rainforest(comparison)
```

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