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Probing omics data via harmonic persistent homology.

Davide Gurnari1, Aldo Guzmán-Sáenz2, Filippo Utro2

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|November 6, 2025
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Summary
This summary is machine-generated.

Topological data analysis (TDA) using harmonic persistent homology can identify molecular biomarkers in complex diseases. This method reveals hidden patterns in multi-omics data for disease subtyping and biomarker discovery.

Keywords:
Data analysisMulti-omicsPattern discoveryTopological data analysis

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Area of Science:

  • Computational Biology
  • Data Science
  • Genomics

Background:

  • Identifying molecular signatures in complex diseases with similar symptoms from high-dimensional multi-omics data is challenging.
  • Topological Data Analysis (TDA) offers a geometric approach to extract higher-order relationships from data.

Purpose of the Study:

  • To apply harmonic persistent homology to multi-omics data for improved biomarker identification.
  • To overcome limitations in cycle representation within TDA for homology classes.

Main Methods:

  • Utilized harmonic persistent homology, a TDA technique, on multi-omics datasets.
  • Applied the method to analyze the geometric structure of high-dimensional data.

Main Results:

  • Discovered hidden patterns and relationships between different omic profiles.
  • Successfully identified biomarkers predictive of disease subtypes in cancer data.
  • Demonstrated effective dissection of multi-omics data for biomarker discovery.

Conclusions:

  • Harmonic persistent homology is effective for multi-omics data analysis.
  • This approach aids in disease subtyping and identifying biomarkers for complex diseases.
  • The method highlights latent biological pathways associated with disease.