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DgeaHeatmap: an R package for transcriptomic analysis and heatmap generation.

Leonie J Lancelle1, Phani S Potru1, Björn Spittau1

  • 1Department of Anatomy and Cell Biology, Medical School OWL, Bielefeld University, Bielefeld 33615, Germany.

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|September 3, 2025
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Summary
This summary is machine-generated.

Researchers can now perform differential gene expression analysis and create heatmaps using the new R package, DgeaHeatmap. This tool offers a flexible, server-independent solution for analyzing Nanostring GeoMx DSP transcriptomic data.

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Area of Science:

  • Genomics and Bioinformatics
  • Molecular Biology

Background:

  • Transcriptomic data analysis, especially from Nanostring GeoMx DSP, requires accessible and flexible tools.
  • Existing web-based tools often lack transparency, customizability, and data security due to server dependence.
  • There is a need for user-friendly bioinformatics tools for researchers with varying levels of expertise.

Purpose of the Study:

  • To introduce DgeaHeatmap, an R package designed for differential gene expression analysis and heatmap generation.
  • To provide a streamlined, server-independent solution for analyzing Nanostring GeoMx DSP transcriptomic data.
  • To enhance flexibility, transparency, and reproducibility in transcriptomic data analysis.

Main Methods:

  • Development of an R package, DgeaHeatmap, with user-friendly functions.
  • Inclusion of tools for preprocessing, filtering, and annotating transcriptomic datasets (both normalized and raw counts).
  • Implementation of Z-score scaling and k-means clustering for heatmap generation.
  • Adaptation of a workflow from GeoMxTools for handling raw Nanostring GeoMx DSP data.

Main Results:

  • DgeaHeatmap offers streamlined functions for differential gene expression analysis and heatmap generation.
  • The package supports both normalized and raw count data, enabling comprehensive analysis.
  • Server-independent analysis enhances flexibility, transparency, and reproducibility for researchers.
  • Customizable heatmaps are generated using Z-score scaling and k-means clustering.

Conclusions:

  • DgeaHeatmap provides an accessible and flexible R package for transcriptomic data analysis.
  • The package empowers researchers, including those with limited bioinformatics experience, to analyze complex data effectively.
  • DgeaHeatmap promotes greater transparency, customizability, and reproducibility in gene expression studies.