$\text{MetaNetis}$ is an package purpose-built a novel tool for metabolomics studies by classifying metabolite samples against a robust, internally-curated baseline derived from the $\text{Human Metabolome Database (HMDB)}$. The core biological data being analyzed are metabolite concentration values (e.g., 12 umol/L of glucose in blood sample) and their functional associations with metabolic pathways. MetaNetis significantly improves the current workflow in computational biology by providing $\text{novel, quantitative functional interpretation}$. Unlike tools such as MetaboAnalyst, which typically rely on pathway enrichment statistics, MetaNetis compares results against $\text{real-world, sample-matched metabolite reference data}$ and directly links individual concentration deviations to pathway activity (Hypoactive/Hyperactive). This unique approach eliminates the interpretation bias inherent in generic comparisons and allows for the precise, quantitative assessment of functional metabolic changes. Development for $\text{MetaNetis}$ was conducted on R version 4.5.1 (2025-06-13 ucrt) using Windows 10 x64 (build 19045).

You can install the development version of MetaNetis from GitHub with:

install.packages("devtools")
library("devtools")
devtools::install_github("dujay971226/MetaNetis", build_vignettes = TRUE, dependencies = TRUE)
library("MetaNetis")

To run the Shiny app:

runMetaNetis()

MetaNetis is an $\text{R}$ package designed to bridge the gap between raw metabolomics data and deep biological interpretation. It provides a novel framework for benchmarking user-supplied metabolite concentrations against a robust, healthy human reference population derived from resources like the Human Metabolome Database ($\text{HMDB}$).

The package’s core value lies in its ability to generate high-confidence classifications and translate those classifications into functional insights about metabolic pathway activity.

Critically, MetaNetis offers robust functionality to evaluate quantitative, clinical sampled data such as blood and urine. This capability is a key differentiator from existing tools; for instance, popular platforms like MetaboAnalyst are often restricted to qualitative analysis, relying solely on pathway enrichment. MetaNetis moves beyond enrichment to provide a direct, directional assessment of metabolic activity (e.g., hyper- or hypo-regulation), making it a powerful tool for metabolomic and clinical research.

ls("package:MetaNetis")
data(package = "MetaNetis") 
browseVignettes("MetaNetis")

MetaNetis contains 7 functions.

1. GetRefRanges for retrieving the established reference concentration ranges sourced from HMDB (Human Metabolome Database), used to classify metabolite values.

2. SetAltBaseline for setting an alternative comparison baseline (instead of the standard reference) for subsequent metabolite analysis.

3. MetabAnalysis for comparing raw metabolite concentration values in samples against reference ranges or baselines to determine if each metabolite is ‘High,’ ‘Low,’ or ‘Normal.’

4. GetPathwayMap for loading the internal data frame that links individual metabolite identifiers to their respective metabolic pathways sourced from HMDB.

5. MapToPathway for aggregating the metabolite concentration status scores for a given sample into a single Net Score for each pathway, indicating pathway activity status.

6. PlotNetwork for generating a visual network graph that displays pathways as nodes, colored by their Net Score, and links them based on the number of shared metabolites.

7. runMetaNetis for executing the shiny interface, including loading sample data, scoring, mapping, and network visualization.

Author and Core Package ContributionsThe package MetaNetis was conceived and primarily developed by $\text{Jay Du}$. Jay’s core contributions include designing the novel quantitative scoring framework that moves beyond traditional enrichment analysis, developing the robust data processing pipeline for standardizing the Human Metabolome Database ($\text{HMDB}$) reference ranges, and implementing the functional pathway scoring logic (MapToPathway and MetabAnalysis). Significant effort was dedicated to creating a modular and testable package structure, ensuring that core data retrieval functions (GetPathwayMap, GetRefRanges) and visualization tools (PlotNetwork) are seamlessly integrated.

Generative AI Tool ContributionsThe generative AI tool, Gemini, served as a key collaborative partner throughout the development of MetaNetis. Its assistance was instrumental in ensuring code stability and clarity across the package. Specifically, Gemini was utilized to generate clear, comprehensive commenting and documentation for all core functions within the package. Furthermore, the AI tool provided essential debugging support for both the and functions, helping to resolve complex issues related to quantitative score aggregation and data matching across disparate input structures, ensuring the reliability of the core analytical pipeline.

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Wishart, D. S., et al. (2022). HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Research, 50(D1), D218–D227. https://hmdb.ca/

This package was developed as part of an assessment for 2025 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. MetaNetis welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.