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leapR: An R Package for Multiomic Pathway Analysis.

Vincent DannaHugh MitchellLindsey AndersonIobani GodinezSara J C GoslineJustin TeeguardenJason E McDermott
Published in: Journal of proteome research (2021)
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 (https://github.com/biodataganache/leapR).
Keyphrases
  • electronic health record
  • high throughput
  • big data
  • drinking water
  • type diabetes
  • data analysis
  • single cell
  • gene expression
  • adipose tissue
  • weight loss
  • deep learning
  • quantum dots
  • rna seq