Single-cell RNA sequencing (scRNA-seq) has been instrumental in recent years in understanding the dynamics of gene expression at the cellular level. It has significantly advanced research on cellular heterogeneity (individual cells may express genes differentially), the discovery of novel cell types, and the understanding of complex biological processes. Researchers commonly use dimensionality reduction techniques, such as t-SNE and UMAP, to visually analyze quantitative data, such as sparse count-based data derived from sequencing data. This paper proposes enhancements to the Integrated Genome Browser (IGB) designed to facilitate the analysis and visualization of aligned nucleotide sequences from scRNA-seq experiments. IGB, an open source desktop-based genome browser, is widely used to visually analyze genome-scale datasets and is particularly suitable for visualizing scRNA-seq data. However, scRNA-seq generates massive files because every RNA molecule in each individual cell is sequenced in samples containing a high number of cells. Rendering such massive datasets in a genome browser can crowd and clutter the plot area, obscuring relevant insights that might be derived from the visualization. I propose including a filtering and coloring mechanism that allows users to selectively visualize sequence reads based on categorical variables (cell identifiers, unique molecular identifiers), quantitative variables (gene expression levels), or other scRNA-seq metrics. This reduces clutter and allows researchers to extract key details and insights from the plot. Additionally, a feature to connect high-dimensional data visualizations, such as UMAP and t-SNE, to IGB enables users to explore clusters or cells of interest and dynamically filter or color corresponding sequence reads for deeper insights. These enhancements would create a unified platform for enhanced data visualization for high-dimensional data, offering researchers the ability to visually validate quantitative data, link dimensionally reduced charts to raw data, and discern distinctive patterns for downstream analysis.
