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Updated: Oct 28, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
Published on: November 10, 2023
Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism
Valentina S Klaus1, Sonja C Schriever2, José Manuel Monroy Kuhn3
1Computational Discovery Research Unit, Institute for Diabetes and Obesity, Helmholtz Zentrum München, Neuherberg, Germany; TUM School of Medicine, Neurobiology of Diabetes, Technical University Munich, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Institute for Diabetes and Obesity, Helmholtz Diabetes Center at Helmholtz Zentrum München, Germany.
Objective:
Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce.
Methods:
We present here a fully unsupervised and versatile correlation-based method - termed Correlation guided Network Integration (CoNI) - to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways.
Results:
By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR).
Conclusion:
Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.
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