EnrichMET: R Package for Integrated Pathway and Network Analysis for Metabolomics

Abstract

Advances in metabolomics have significantly improved our understanding of cellular processes by enabling the identification of hundreds of metabolites in a single experiment. These developments provide valuable insights into complex metabolic networks. While efforts have been made to develop pathway enrichment analysis (PEA), existing implementation often require multiple steps, rely on web-based interfaces, or depend on R packages configuration that may affect reproducibility and ease of use. To overcome these limitations, we introduce EnrichMet, an R package for fast, flexible, and reproducible pathway enrichment analysis. EnrichMet modules support over-representation analysis of pathways, metabolite set enrichment analysis (MetSEA), and network-based pathway analysis. The package streamlines the workflow by combining curated pathway information from the Kyoto Encyclopedia of Genes and Genomics (KEGG) and employs Fishers Exact Test to identify significantly enriched pathways. Benchmark analyses show that enrichment on sample data completes in approximately 3 seconds. EnrichMet offers both a command-line and a user-friendly Shiny interface, enabling accessibility for users with or without programming experience. Through case studies on experimental metabolomics datasets, we demonstrated that EnrichMet delivers accurate and comprehensive pathway enrichment results while minimizing computational time and simplifying user interaction. Furthermore, its flexible framework supports extensions to other data types and knowledge bases beyond KEGG, as illustrated through a lipidomics case study. By unifying performance, reproducibility, usability, and visualization within a single package, EnrichMet facilitates deeper insights and promotes efficient, transparent, and reproducible research practices. Availability and implementation(https://github.com/biodatalab/enrichmet.git)