ShinyCell

ShinyCell provides interactive visualizations of single-cell RNA sequencing (scRNA-seq) datasets to facilitate exploratory analysis of cell-level gene expression and associated metadata.

Key Features:

• R and Shiny integration: Uses the R programming environment and the Shiny framework to generate dynamic visualizations of scRNA-seq data.
• Dimensionality reduction visualizations: Visualizes cell metadata and gene expression on reduced-dimension embeddings such as Uniform Manifold Approximation and Projection (UMAP).
• Distribution analysis: Plots distributions of continuous cellular metrics, including normalized Unique Molecular Identifiers (nUMI) and module scores, using violin plots and box plots.
• Gene expression visualization: Displays multi-gene expression using bubble plots and heatmaps for comparative analysis across cell populations.

Scientific Applications:

• Exploratory single-cell analysis: Enables interactive examination of cell-level gene expression patterns in scRNA-seq datasets.
• Comparative gene expression profiling: Supports comparison of multiple genes across cell populations using bubble plots and heatmaps.
• Assessment of cellular metrics: Facilitates evaluation of variability in metrics such as nUMI and module scores via violin and box plots.
• Large-scale and collaborative projects: Provides visualizations suitable for multi-center collaborations and large-scale genomic projects requiring single-cell data inspection.

Methodology:

Transforms scRNA-seq datasets into interactive visualizations by integrating R's computational functionality with the Shiny framework.