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ENViz: a Cytoscape App for integrated statistical analysis and visualization of sample-matched data with multiple

Israel Steinfeld1, Roy Navon2, Michael L Creech2

  • 1Agilent Laboratories, Tel-Aviv, Israel, Technion - Israel Institute of Technology, Haifa, Israel, Blue Oak Software and Agilent Laboratories, Santa Clara, CA, USA Agilent Laboratories, Tel-Aviv, Israel, Technion - Israel Institute of Technology, Haifa, Israel, Blue Oak Software and Agilent Laboratories, Santa Clara, CA, USA.

Bioinformatics (Oxford, England)
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PubMed
Summary

ENViz performs joint enrichment analysis on matched datasets, linking biological data to annotations. This Cytoscape app visualizes significant associations, aiding in understanding complex biological processes.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • High-throughput biological data, such as gene expression, are often collected from matched samples.
  • Analyzing these datasets requires integrating them with systematic annotations (e.g., pathways, gene ontology) to derive meaningful biological insights.
  • Existing tools may not efficiently handle joint analysis of multiple matched datasets within a comprehensive annotation framework.

Purpose of the Study:

  • To introduce ENViz (Enrichment Analysis and Visualization), a Cytoscape application designed for joint enrichment analysis.
  • To enable the integrated analysis of two sample-matched datasets against diverse annotation sources.
  • To provide a visual and interactive platform for exploring the results of enrichment analyses.

Main Methods:

  • ENViz utilizes Cytoscape, a popular open-source platform for visualizing complex biological networks.
  • The app performs joint enrichment analysis by comparing elements from two datasets against user-defined or public annotation databases.
  • Results are presented as interactive networks, highlighting significant associations between data elements and annotation terms.

Main Results:

  • ENViz successfully identifies significant associations between profiled elements in one dataset and annotation terms, considering a second matched dataset.
  • The application visualizes these findings as interactive networks within Cytoscape, facilitating exploration.
  • Demonstrated utility in identifying biologically relevant links, such as microRNA associations with cellular processes in specific cancer types.

Conclusions:

  • ENViz offers a powerful and integrated approach for joint enrichment analysis of matched biological datasets.
  • The visualization capabilities within Cytoscape enhance the interpretability of complex biological associations.
  • This tool supports researchers in uncovering novel biological insights from multi-omics and other high-throughput data.