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Updated: May 5, 2026

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

supraHex: an R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map.

Hai Fang¹, Julian Gough¹

¹Computational Genomics Group, Department of Computer Science, University of Bristol, Merchant Venturers Building, Bristol BS8 1UB, UK.

Biochemical and Biophysical Research Communications

|December 7, 2013

Summary

This summary is machine-generated.

The supraHex package offers a novel way to analyze and visualize omics data, enabling biologists to quickly understand complex genomic datasets. It facilitates gene clustering and sample correlation, revealing relationships between DNA replication timing, CpG density, and gene expression.

Keywords:

Bioinformatics Clustering DNA replication timing Gene expression Sample correlation Visualisation

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Last Updated: May 5, 2026

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Published on: November 3, 2023

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Biologists face challenges in analyzing large-scale genomic datasets with many features (genes) and few samples.
Existing methods may not efficiently handle the unique shape of omics data for rapid understanding.

Purpose of the Study:

To introduce 'supraHex', an open-source package for training, analyzing, and visualizing omics data.
To provide a scalable framework for multilayer omics data comparison and interpretation.

Main Methods:

Development of a supra-hexagonal map for self-organizing omics data.
Implementation of scalable post-analysis functionalities for the map.
Integration of data overlay capabilities for multilayer comparisons.

Main Results:

Demonstrated simultaneous gene clustering and sample correlation using DNA replication timing data in mouse embryogenesis.
Provided intuitive visualizations throughout the analysis process.
Revealed inherent relationships between late replication, low CpG density promoters, and low expression levels by overlaying CpG and expression data.

Conclusions:

supraHex enables ultrafast understanding of tabular omics data for a wide scientific community.
The package offers a scientifically and artistically intuitive framework for complex data analysis.
supraHex is freely available, cross-platform, and supported by tutorials.