alexa NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Jianguo Xia, Erin E Gill, Robert E W Hancock

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Meta-analysis of gene expression data sets is increasingly performed to help identify robust molecular signatures and to gain insights into underlying biological processes. The complicated nature of such analyses requires both advanced statistics and innovative visualization strategies to support efficient data comparison, interpretation and hypothesis generation. NetworkAnalyst (http://www.networkanalyst.ca) is a comprehensive web-based tool designed to allow bench researchers to perform various common and complex meta-analyses of gene expression data via an intuitive web interface. By coupling well-established statistical procedures with state-of-the-art data visualization techniques, NetworkAnalyst allows researchers to easily navigate large complex gene expression data sets to determine important features, patterns, functions and connections, thus leading to the generation of new biological hypotheses. This protocol provides a step-wise description of how to effectively use NetworkAnalyst to perform network analysis and visualization from gene lists; to perform meta-analysis on gene expression data while taking into account multiple metadata parameters; and, finally, to perform a meta-analysis of multiple gene expression data sets. NetworkAnalyst is designed to be accessible to biologists rather than to specialist bioinformaticians. The complete protocol can be executed in ~1.5 h. Compared with other similar web-based tools, NetworkAnalyst offers a unique visual analytics experience that enables data analysis within the context of protein-protein interaction networks, heatmaps or chord diagrams. All of these analysis methods provide the user with supporting statistical and functional evidence.

This article was published in Nature Protocols and referenced in International Journal of Biomedical Data Mining

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