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netgsa: Fast computation and interactive visualization for topology-based pathway enrichment analysis

Michael Hellstern, Jing Ma, Kun Yue, Ali Shojaie

2021PLoS Computational Biology12 citationsDOIOpen Access PDF

Abstract

Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods' capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization.

Topics & Concepts

Computer scienceLeverage (statistics)VisualizationSoftwareInteractive visualizationComputationData miningNetwork topologyData visualizationTheoretical computer scienceDistributed computingMachine learningProgramming languageComputer networkBioinformatics and Genomic NetworksGene expression and cancer classificationGene Regulatory Network Analysis
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