leapR: An R Package for Multiomic Pathway Analysis

A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (fittps:/,/githithcornibiodataganache/leapR).

Automated summary

The goal of high-throughput data studies is to identify functional mechanisms underlying biological phenomena, which requires time-consuming evaluation of various statistical methods. The leapR package allows rapid assessment of biological pathway activity, facilitating integration of multisource data.