DoTools: a cross platform framework to streamline common single cell workflows.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding biological conditions.
• The proliferation of analysis tools across different programming languages complicates scRNA-seq data integration and workflow development.
• Significant coding expertise and learning time are often required to combine various scRNA-seq analysis tools.

Purpose of the Study:

• To develop a unified framework, DoTools, that simplifies the integration of diverse third-party scRNA-seq analysis tools.
• To provide a solution that bridges R/Bioconductor and Python/PyPI environments for seamless scRNA-seq data analysis.
• To enhance accessibility and efficiency in scRNA-seq data preprocessing, quality control, cell type annotation, and downstream analysis.

Main Methods:

• Developed DoTools, a cross-language framework compatible with R/Bioconductor and Python/PyPI.
• Implemented wrappers for integrating popular tools like scVI, CellTypist, and CellBender.
• Incorporated visualization utilities and best practices for scRNA-seq analysis pipelines (e.g., Seurat, SingleCellExperiment, Scanpy).

Main Results:

• DoTools successfully integrates multiple third-party tools into unified scRNA-seq analysis workflows.
• The framework streamlines essential analysis steps, including data preprocessing, quality control, and cell type annotation.
• DoTools supports both novice and experienced users by providing an accessible and modular approach to complex scRNA-seq analysis.

Conclusions:

• DoTools significantly reduces the complexity and technical barriers associated with integrating diverse scRNA-seq analysis tools.
• The framework promotes standardized and efficient scRNA-seq data analysis across different computational environments.
• DoTools enhances the accessibility of advanced scRNA-seq analysis techniques for a broader scientific community.