leapR

leapR performs statistical enrichment analysis to identify pathway-level mechanisms in single- and multi-omics datasets spanning genomic, transcriptomic, proteomic, and metabolomic data.

Key Features:

• R package implementation: Implemented as an R package for programmatic analysis.
• Statistical enrichment analysis: Performs pathway-centric statistical enrichment analysis on input datasets.
• Multi-omics support: Accepts and processes single- or multi-omics datasets from genomic, transcriptomic, proteomic, and metabolomic sources.
• Data integration: Integrates multisource high-throughput data for joint pathway analysis across omics layers.
• Diverse statistical tests: Incorporates diverse statistical tests to evaluate enrichment across multiple data sources.
• Pathway activity assessment: Enables assessment of biological pathway activity across different omics layers.
• Hypothesis summarization: Summarizes complex multi-omics datasets into testable hypotheses for downstream investigation.

Scientific Applications:

• Mechanism discovery: Identify functional mechanisms underlying disease outcomes and metabolic processes.
• Cross-omics pathway analysis: Compare and assess pathway activity across genomic, transcriptomic, proteomic, and metabolomic layers.
• Hypothesis generation: Generate testable hypotheses from integrated multi-omics datasets for experimental follow-up.

Methodology:

Performs statistical enrichment analysis and rapid assessment of biological pathway activity by integrating single- and multi-omics high-throughput data and applying diverse statistical tests while summarizing results into testable hypotheses.