ShIVA: a user-friendly and interactive interface giving biologists control over their single-cell RNA-seq data.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell technologies, including single-cell RNA sequencing (scRNA-seq) and CITE-seq, are transforming biological research.
• The increasing complexity of multi-omics and spatially resolved single-cell data presents significant analytical challenges.
• Biologists often lack the specialized data analysis resources required to interpret these complex datasets.

Purpose of the Study:

• To introduce ShIVA, a user-friendly interface designed for the analysis of scRNA-seq and CITE-seq data.
• To empower biologists with a tool for robust, reproducible, and intuitive single-cell data exploration.
• To facilitate the generation of key figures and tables for further expert analysis.

Main Methods:

• Development of ShIVA, an interface built upon the Seurat v4 R package.
• Integration of intuitive workflows and comprehensive video tutorials.
• Focus on enabling biologists to perform self-guided data analysis and exploration.

Main Results:

• ShIVA allows biologists to explore and quantify their single-cell datasets effectively.
• The interface supports the generation of essential figures and tables for data interpretation.
• Users can export their analyzed data for more advanced downstream analyses by experts.

Conclusions:

• ShIVA democratizes single-cell data analysis for biologists lacking specialized bioinformatics support.
• The tool promotes reproducible research by guiding users through a standardized analysis process.
• ShIVA enhances the accessibility and utility of scRNA-seq and CITE-seq data in biological research.