Abstract
While many methods exist for integrating multi-omics data or defining gene sets, there is no one single tool that defines gene sets based on merging of multiple omics data sets. We present shinyGISPA, an open-source application with a user-friendly web-based interface to define genes according to their similarity in several molecular changes that are driving a disease phenotype. This tool was developed to help facilitate the usability of a previously published method, Gene Integrated Set Profile Analysis (GISPA), among researchers with limited computer-programming skills. The GISPA method allows the identification of multiple gene sets that may play a role in the characterization, clinical application, or functional relevance of a disease phenotype. The tool provides an automated workflow that is highly scalable and adaptable to applications that go beyond genomic data merging analysis. It is available at http://shinygispa.winship.emory.edu/shinyGISPA/.
Highlights
Identification of driver genes in a sample phenotype remains quintessential in cancer genomics research for which expression data has been typically analyzed to identify genes with significant differences between groups of similar phenotypes
It is even more important to incorporate changes from all molecular levels of high-dimensional data to proper understanding of multiple factors driving the tumor growth. This helps improve efficiency of predicting oncogenes or tumor suppressor genes associated with pathogenesis compared to genes based on individual data sets
Multiple molecular targets are needed to improve drug efficacy against cancer cells by targeting multiple molecular mechanisms, for instance, DNA methylation, an epigenetic mechanism often modify the function of the genes by regulating gene expression
Summary
Identification of driver genes in a sample phenotype remains quintessential in cancer genomics research for which expression data has been typically analyzed to identify genes with significant differences between groups of similar phenotypes. A result merging approach is the simpler of the two and the more popular With this approach, genes are defined as statistically significant within each data type and combined among data types by their intersection. The result is a list of genes that are commonly identified as significantly different among phenotypes resulting from several independent analyses of each data type and thought to collectively explain substantial phenotypic changes. This approach suffers from the major limitation that, as the number of data types increases, the intersection becomes smaller and smaller and, it is not scalable.
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