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

Michael Hellstern | Zendy; Jing Ma | Zendy; Kun Yue | Zendy; Ali Shojaie | Zendy

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

Author(s) -

Michael Hellstern,

Jing Ma,

Kun Yue,

Ali Shojaie

Publication year - 2021

Publication title -

plos computational biology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.628

H-Index - 182

eISSN - 1553-7358

pISSN - 1553-734X

DOI - 10.1371/journal.pcbi.1008979

Subject(s) - computer science , leverage (statistics) , visualization , software , interactive visualization , computation , data mining , network topology , data visualization , theoretical computer science , distributed computing , machine learning , programming language , computer network

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.

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