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Related Experiment Video

Updated: Jan 5, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal.

Shirley Pepke1, William M Nelson2, Greg Ver Steeg3

  • 1Lyrid LLC; spepke@lyridllc.com.

Journal of Visualized Experiments : Jove
|October 29, 2019
PubMed
Summary

CorExplorer is a new website that helps researchers analyze complex gene expression data from tumors. It uses the CorEx machine learning algorithm to identify patterns and integrates them with other biological data for better understanding of cancer.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Differential gene expression analysis is crucial for understanding disease mechanisms, particularly in oncology.
  • The CorEx machine learning algorithm aids in analyzing gene expression patterns in tumor RNA-sequencing (RNA-seq) data.
  • Interpreting the numerous factors generated by CorEx can be challenging for researchers.

Purpose of the Study:

  • To develop an interactive website, CorExplorer, for easier exploration and interpretation of CorEx-derived factors from tumor RNA-seq data.
  • To facilitate the connection of CorEx factors to existing biological knowledge and clinical data.

Main Methods:

  • Trained the CorEx algorithm on RNA-seq gene expression data from four tumor types: ovarian, lung, melanoma, and colorectal.
  • Integrated external data including survival information, protein-protein interactions, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments, and heatmaps.
  • Developed a website featuring interactive factor graph visualization for data exploration.

Main Results:

  • CorExplorer provides an interactive platform to visualize and analyze CorEx factors.
  • The website integrates multiple data types, enabling association of learned tumor factors with biological context.
  • Example protocols demonstrate the utility of CorExplorer for comprehending the significance of tumor factors.

Conclusions:

  • CorExplorer enhances the utility of the CorEx algorithm for precision oncology research.
  • The interactive platform simplifies the interpretation of complex gene expression patterns in tumors.
  • This tool aids researchers in connecting machine learning findings to established biological knowledge.