SeuratIntegrate: an R package to facilitate the use of integration methods with Seurat

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) analysis increasingly involves integrating multiple datasets, including complex single-cell atlases with nested batch effects.
• Current tools like Seurat have limited batch-correction method diversity, making optimal integration approach selection challenging due to dataset-specific performance variations.

Purpose of the Study:

• To introduce SeuratIntegrate, an R package that broadens the integration methods available for Seurat users.
• To provide a comprehensive suite of tools for comparative integration analysis and benchmarking within the R environment.

Main Methods:

• Developed SeuratIntegrate, an open-source R package built upon Seurat's framework.
• Integrated diverse integration methods, including Python-based approaches, accessible within R.
• Implemented features for automated Python environment management, cross-language object conversion, and score handling/visualization for benchmarking.

Main Results:

• SeuratIntegrate expands the repertoire of scRNA-seq integration methods accessible to Seurat users.
• The package facilitates robust integration benchmarking using established performance metrics.
• Automated environment management and cross-language compatibility streamline complex integration workflows.

Conclusions:

• SeuratIntegrate enhances flexibility and effectiveness in single-cell data integration workflows.
• The package supports informed decision-making for selecting appropriate integration strategies.
• Open accessibility via GitHub and Zenodo promotes reproducible research and broader adoption.