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.
Highlights
Pathway enrichment analysis methods have become standard tools for analyzing omics data [1]
The three key improvements included dramatically reduced computation time so pathway enrichment can be performed within minutes on a personal computer, integration of publicly available pathway topology databases so users can leverage the entire capabilities of the NetGSA method, and facilitating interactive visualization of results through an interface with Cytoscape, a popular network visualization tool
The improved NetGSA was compared to the previous version as well as other similar pathway topology-based methods and achieves competitive statistical power
Summary
Pathway enrichment analysis methods have become standard tools for analyzing omics data [1]. While earlier generations of methods are still widely used, the third generation, topologybased methods, may offer advantages by incorporating the pathway structure [2]. Despite these advantages, limitations in existing methods and software have hindered wide adoption of topology-based methods [1]. Computationally efficient methods, such as SPIA [6] and PRS [7] require differentially expressed genes which may or may not be detected Methods such as topologyGSA [8] and Pathway-Express [9] have specific input requirements and may not be applicable to, e.g., metabolomics data [5]. This information is often spread across several databases, such as KEGG [10], Reactome [11], and PantherDB [12], making aggregation challenging for non-expert users
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