SCelVis

SCelVis provides interactive visualization and exploratory analysis of pre-processed single-cell omics data to support interpretation of cellular heterogeneity and gene expression patterns.

Key Features:

• Interactive visualization: Provides interactive visualizations for pre-processed single-cell expression data and cell annotations.
• Cell grouping and differential expression: Enables definition of cell groups by filtering or manual selection and performs differential gene expression analysis within the application.
• Data access and integration: Accepts input from local and remote sources using standard and open protocols to integrate with existing workflows.
• Data privacy and FAIR principles: Supports FAIR data management and includes measures for data privacy and security when handling clinical data across institutions.
• Implementation and extensibility: Implemented in Python using Dash by Plotly and released under the MIT license, enabling customization and integration with third-party pipelines.
• Validation: Functionality validated using publicly available single-cell RNA sequencing (scRNA-seq) data.

Scientific Applications:

• Single-cell data exploration: Facilitates exploration of high-dimensional single-cell omics datasets to reveal cellular heterogeneity and gene expression patterns.
• Translational and clinical research: Supports biomedical and clinical applications by enabling analysis of clinical single-cell datasets across institutions.

Methodology:

Implemented in Python using Dash by Plotly to provide dynamic, web-based interactive visualizations.