scExploreR: a flexible platform for democratized analysis of multimodal single-cell data by non-programmers.

Area of Science:

• Biomedical research
• Computational biology
• Genomics

Background:

• Single-cell sequencing reveals cellular heterogeneity crucial for understanding disease mechanisms and advancing personalized medicine.
• Current single-cell data analysis tools often require programming expertise, limiting accessibility for many researchers.
• Existing graphical user interfaces for single-cell data analysis have limitations in analytical depth and data format flexibility.

Purpose of the Study:

• To develop an accessible tool, scExploreR, that enables non-programmers to perform comprehensive single-cell data analysis.
• To extend the range of analytical tasks available to researchers without extensive programming experience.
• To facilitate direct exploration and analysis of single-cell data, bridging the gap between biological and computational scientists.

Main Methods:

• Developed scExploreR as a packaged R Shiny application for local or server deployment.
• Integrated SCUBA package to seamlessly handle multimodal single-cell data.
• Implemented extensive customization options for generating publication-quality figures.

Main Results:

• scExploreR provides a user-friendly interface for single-cell data analysis, reducing the need for programming skills.
• The application supports multimodal data analysis with consistent plotting capabilities across different input formats.
• Users can generate highly customized, publication-ready figures directly within the scExploreR environment.

Conclusions:

• scExploreR significantly lowers the barrier to entry for single-cell data analysis, democratizing access to powerful insights.
• The tool enhances collaboration between biologists and computational scientists by providing a common, accessible platform.
• scExploreR streamlines the generation of insights from complex single-cell datasets, accelerating biomedical research and personalized medicine.