Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Inter-tissue relationships of gene expression in liver, muscle and adipose tissue of children with end-stage chronic liver disease.

Journal of pediatric gastroenterology and nutrition·2026
Same author

The heterogeneity of dopamine-mediated vasodilation in human intrarenal arteries.

Journal of advanced research·2026
Same author

Phospho-JNK agonists show promising effects for the treatment of hepatocellular carcinoma.

iScience·2026
Same author

IgLON5 autoimmune antibodies activate Tau via neuronal hyperactivity.

Science advances·2026
Same author

WBT-DC pipeline: a cross-cohort and cross-platform disease classification pipeline based on whole-blood transcriptomics.

Journal of translational medicine·2026
Same author

Flexible Strain-Temperature Dual-Modal Smart Patch Based on 3D Printing for Wound Healing Promotion and Health Monitoring.

ACS applied materials & interfaces·2026

Related Experiment Video

Updated: Aug 30, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K

hCoCena: horizontal integration and analysis of transcriptomics datasets.

Marie Oestreich1,2, Lisa Holsten2,3, Shobhit Agrawal2,4

  • 1Modular High Performance Computing and Artificial Intelligence, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany.

Bioinformatics (Oxford, England)
|August 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces horizontal CoCena (hCoCena), an R-package for gene co-expression network analysis. hCoCena enables joint analysis of multiple transcriptomic datasets, facilitating a deeper understanding of gene function across conditions.

More Related Videos

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

13.7K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.8K

Related Experiment Videos

Last Updated: Aug 30, 2025

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

13.7K
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
09:58

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis

Published on: June 27, 2020

2.8K

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene co-expression analysis is crucial for understanding biological conditions.
  • Analyzing multiple transcriptomic datasets together is challenging but necessary for comprehensive insights.
  • Existing tools often lack the flexibility and user-friendliness for integrative analysis.

Purpose of the Study:

  • To introduce horizontal CoCena (hCoCena), a novel R-package for gene co-expression network analysis.
  • To provide a user-friendly and adaptable tool for both single and multi-dataset transcriptomic analyses.
  • To enable systematic understanding of gene co-expression and co-functionality across diverse biological conditions.

Main Methods:

  • Development of the hCoCena R-package for network-based co-expression analysis.
  • Implementation of an exemplary analysis workflow using R Markdowns.
  • Comparative analysis against existing co-expression analysis tools.

Main Results:

  • hCoCena facilitates the joint analysis of multiple transcriptomic datasets.
  • The R-package offers a user-friendly, flexible, and customizable platform for integrative analysis.
  • hCoCena provides extensive documentation and an exemplary workflow for seamless integration.

Conclusions:

  • hCoCena enhances the ability to perform integrative multi-study transcriptomic analyses.
  • The tool democratizes advanced gene co-expression analysis for researchers with varying programming expertise.
  • hCoCena is freely available on GitHub with comprehensive documentation and support.