Frontend visualization and incorporation of single cell data in cBioPortal

My project aims to bridge the gap between single-cell gene expression data analysis and existing cancer genomics workflows, particularly within the cBioPortal platform. Currently, cBioPortal lacks support for visualizing single-cell data alongside bulk genomics data, hindering researchers from gaining a comprehensive understanding of cancer biology. To address this, I propose the development of a dedicated single-cell analysis module within cBioPortal, focusing on the MSK Spectrum dataset. The key problem I am addressing is the inability to integrate single-cell gene expression data with existing cancer genomics workflows. By incorporating single-cell data into cBioPortal, researchers will be empowered to identify specific cells with altered gene expression, understand their contribution to overall expression patterns, and connect this information with genetic alterations. My solution involves developing a custom Single-Cell Tab within cBioPortal's study view. The tab will include features such as dataset information, gene selection, interactive visualizations (boxplots, scatter plots, stacked bar graphs, and pie charts), filtering options, and download capabilities. To achieve this, I will aggregate the single-cell data to a gene-cell type-sample level, creating a pseudo bulk representation to manage the large dataset size effectively. I will utilize existing charting libraries like CanvasJS and React primitives for visualization components, ensuring seamless integration with cBioPortal's design language. My deliverables include: Custom Single-Cell Tab within cBioPortal study view. Dataset information panel for study description and cell type/sample details. Gene selection panel with autocomplete functionality. Interactive visualizations including boxplots, scatter plots, stacked bar graphs, and pie charts. Filtering options for focused data exploration. Download options for data and plots in common file formats.