Identifying associations between patient gene expression profiles and clinical data provides insight into the biological processes associated with health and disease. The Gene Expression Omnibus (GEO) is a public repository of gene expression and sequence-based datasets, and currently includes >42,000 datasets with gene expression profiles obtained by microarray. Although GEO has its own analysis tool (GEO2R) for identifying differentially expressed genes, the tool is not designed for advanced data analysis and does not generate publication-ready graphics. In this work, we describe a web-based, easy-to-use tool for biomarker analysis in GEO datasets, called shinyGEO.
shinyGEO is a web-based tool that provides a graphical user interface for users without R programming experience to quickly analyze GEO datasets. The tool is developed using 'shiny', a web application framework for R. Specifically, shinyGEO allows a user to download the expression and clinical data from a GEO dataset, to modify the dataset correcting for spelling and misaligned data frame columns, to select a gene of interest, and to perform a survival or differential expression analysis using the available data. The tool uses the Bioconductor package 'GEOquery' to retrieve the GEO dataset, while survival and differential expression analyses are carried out using the 'survival' and 'stats' packages, respectively. For both analyses, shinyGEO produces publication-ready graphics using 'ggplot2' and generates the corresponding R code to ensure that all analyses are reproducible. We demonstrate the capabilities of the tool by using shinyGEO to identify diagnostic and prognostic biomarkers in cancer.
Formerly a Graduate MS Predictive Analytics student at DePaul University; Currently a Graduate MS student at Johns Hopkins Engineering For Professionals studying Computer Science and Data Science. Currently an Associate Data Scientist at The Hartford Insurance Group working on building... Read More →
