https://github.com/PNNL-CompBio/leapR

https://github.com/PNNL-CompBio/leapR

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/PNNL-CompBio/leapR
• Owner: pnnl
• License: bsd-3-clause
• Created: 2020-11-02T19:51:43.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2024-01-02T17:55:35.000Z (11 months ago)
• Last Synced: 2024-08-23T08:02:07.288Z (3 months ago)
• Language: R
• Size: 46.9 MB
• Stars: 17
• Watchers: 2
• Forks: 5
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-proteomics - leapR - R - package for multiple pathway analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c00963) (7. Protein Pathway Enrichment / Table of Contents)

README

        
#leapR

Layered Enrichment Analysis of Pathways in R (leapR) a tool that carries out statistical enrichment analysis on single- or multi-omics data.

## Installation

To install leapR, you can use the `devtools` package as follows:

``` R

install.packages("devtools")

devtools::install_github("PNNL-CompBio/leapR",build_vignette=TRUE)

```

Once you have successfully installed the package you can load the vignette to read examples using the `vignette('leapR')` command.

## Basic Usage

The primary function of the `leapR` package is the `leapR` function itself. This function serves a wrapper to run different styles of enrichment functions on the data. The package contains other functions to support pathway information and multi-omics datasets.

### Enrichment calls

Here is a list of enrichment arguments that can be called with the `leapR` command.

| Argument                                        | Description |   

| ---                                             | ----        |   

| `enrichment_in_sets`                           | Calculates enrichment in pathway membership in a list (e.g. highly differential proteins) relative to background using Fisher's exact test. |   

| `enrichment_in_order`                         | Calculates enrichment of pathways based on a ranked list using the Kologmorov-Smirnov test |   

| `enrichment_comparison`                         | Compares the distribution of abundances between two sets of conditions for each pathway using a t test  |

| `enrichment_in_pathways`                        | Compares the distribution of abundances in a pathway with the background distribution of abundances using a t test |   

|`correlation_enrichment`              | Calculates the enrichment of a pathway based on correlation between pathway members across conditions versus correlation between members not in the pathway             |   

| `enrichment_in_relationships`| Calculates the enrichment of a pathway in specified interactions relative to non-pathway members |

### Data examples

We included examples of including proteomics data and transcriptomics data from 169 high-grade serous ovarian cancer (HGSOC) tumors previously studied and lists of the short- and long- surviving patients from that cohort.

### Gene pathway examples

We included two different gene pathways. An NCI pathway database (Pathway Information Database; PID) of signaling pathways and the MSIGDB set of gene collections from various sources.