#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:

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.