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hCoCena: A toolbox for network-based co-expression analysis and horizontal integration of transcriptomic datasets.

Lisa Holsten1, Kilian Dahm2, Marie Oestreich3

  • 1Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany; PRECISE Platform for Single Cell Genomics and Epigenomics, DZNE, and University of Bonn, 53127 Bonn, Germany; Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany; Department of Pediatrics, University Hospital Würzburg, 97080 Würzburg, Germany.

STAR Protocols
|March 1, 2024
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Summary

This study introduces hCoCena, a user-friendly toolbox for analyzing transcriptomic data. It enables both single and multiple dataset analyses, including gene clustering and enrichment analyses.

Keywords:
BioinformaticsComputer sciencesGenomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Increasing complexity and volume of transcriptomic datasets necessitate advanced analysis tools.
  • Existing tools may lack user-friendliness or the capacity for joint multi-dataset analysis.

Purpose of the Study:

  • To present hCoCena, a novel toolbox for transcriptomic data analysis.
  • To provide a user-friendly platform for analyzing single and multiple transcriptomic datasets.
  • To facilitate co-expression network construction and subsequent analyses.

Main Methods:

  • Development of the hCoCena toolbox.
  • Description of workspace setup and data formatting procedures.
  • Implementation of network integration, gene clustering, and enrichment analyses (gene set and transcription factor).

Main Results:

  • hCoCena offers a streamlined workflow for transcriptomic data analysis.
  • The toolbox supports the joint analysis of multiple datasets, enabling comparative studies.
  • Includes modules for gene clustering and comprehensive enrichment analyses.

Conclusions:

  • hCoCena provides an accessible and efficient solution for transcriptomic data analysis.
  • Facilitates the construction and analysis of co-expression networks across datasets.
  • A valuable resource for researchers working with large-scale transcriptomic data.