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PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia.

Kathleen M Chen1, Jie Tan2, Gregory P Way1

  • 11Department of Systems Pharmacology and Translational Therapeutics. Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA.

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Summary
This summary is machine-generated.

PathCORE-T identifies functional pathway-pathway relationships from gene expression data, creating a network of biological processes. This framework reveals new connections and aids in understanding complex biological systems.

Keywords:
CrosstalkGene expressionPathway interactionsUnsupervised feature construction

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Genome-wide data analysis often focuses on individual gene pathways.
  • Interactions between pathways are crucial but challenging to map comprehensively.
  • Existing methods for identifying pathway relationships are limited in scope and scale.

Purpose of the Study:

  • To develop a computational framework, PathCORE-T, for identifying and visualizing transcriptional relationships between biological pathways.
  • To enable the construction of a global network of pathway-pathway interactions across diverse biological conditions.
  • To provide a tool for exploring functional co-occurrence of pathways in large gene expression datasets.

Main Methods:

  • PathCORE-T implements existing methods to detect pathway-pathway transcriptional relationships within a large data compendium.
  • It analyzes features generated by machine learning algorithms (e.g., NMF) applied to gene expression data.
  • The framework identifies pairs of pathways that co-occur in features more than expected by chance, defining them as functionally co-occurring.

Main Results:

  • PathCORE-T successfully identified functionally co-occurring pathways from both microbial and human cancer (TCGA) datasets.
  • The framework generated a network visualizing these global pathway relationships.
  • A web interface was developed for users to explore the network and associated gene expression data.

Conclusions:

  • PathCORE-T effectively identifies and visualizes transcriptionally co-occurring pathways using unsupervised analysis of gene expression data.
  • The framework successfully recapitulated known pathway relationships and proposed novel, testable hypotheses.
  • PathCORE-T offers a valuable tool for advancing systems biology research by mapping complex pathway interactions.