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Updated: Dec 17, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

Identifying persistent structures in multiscale 'omics data.

Fan Zheng^1,2, She Zhang^3,2, Christopher Churas¹

¹Division of Genetics, Department of Medicine, University of California, San Diego, CA 92093, USA.

Biorxiv : the Preprint Server for Biology

|June 27, 2020

Summary

This summary is machine-generated.

This study introduces HiDeF, a novel method using persistent homology to find robust data structures across all scales. It enhances cell type discovery in transcriptomics and reveals SARS-CoV-2

Keywords:

community detection multiscale persistent homology protein-protein interaction network resolution single-cell clustering systems biology

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Area of Science:

Computational Biology
Mathematical Topology
Systems Biology

Background:

Omics data analysis scale impacts outcome (e.g., cell type discovery, protein network analysis).
Current methods may be limited to specific scales, potentially missing robust structures.
Identifying structures across all scales is crucial for comprehensive biological insights.

Conclusions:

Persistent homology offers a powerful approach for multi-scale data analysis in omics.
HiDeF provides a robust framework for discovering biological insights previously missed by scale-specific methods.
The HiDeF method is publicly available for Python and Cytoscape users.