Litcius/Paper detail

STAGEs: A web-based tool that integrates data visualization and pathway enrichment analysis for gene expression studies

Clara WT Koh, Justin S. G. Ooi, Eugenia Z. Ong, Kuan Rong Chan

2023Scientific Reports11 citationsDOIOpen Access PDF

Abstract

Gene expression profiling has helped tremendously in the understanding of biological processes and diseases. However, interpreting processed data to gain insights into biological mechanisms remain challenging, especially to the non-bioinformaticians, as many of these data visualization and pathway analysis tools require extensive data formatting. To circumvent these challenges, we developed STAGEs (Static and Temporal Analysis of Gene Expression studies) that provides an interactive visualisation of omics analysis outputs. Users can directly upload data created from Excel spreadsheets and use STAGEs to render volcano plots, differentially expressed genes stacked bar charts, pathway enrichment analysis by Enrichr and Gene Set Enrichment Analysis (GSEA) against established pathway databases or customized gene sets, clustergrams and correlation matrices. Moreover, STAGEs takes care of Excel gene to date misconversions, ensuring that every gene is considered for pathway analysis. Output data tables and graphs can be exported, and users can easily customize individual graphs using widgets such as sliders, drop-down menus, text boxes and radio buttons. Collectively, STAGEs is an integrative platform for data analysis, data visualisation and pathway analysis, and is freely available at https://kuanrongchan-stages-stages-vpgh46.streamlitapp.com/ . In addition, developers can customise or modify the web tool locally based on our existing codes, which is publicly available at https://github.com/kuanrongchan/STAGES .

Topics & Concepts

UploadDisk formattingVisualizationComputer scienceData visualizationWeb applicationProfiling (computer programming)Gene expression profilingBar chartData miningWorld Wide WebBioinformaticsComputational biologyGeneGene expressionBiologyProgramming languageOperating systemGeneticsMathematicsStatisticsBioinformatics and Genomic NetworksGene Regulatory Network AnalysisGene expression and cancer classification