scExplorer: a comprehensive web server for single-cell RNA sequencing data analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) analysis requires specialized computational skills, posing a barrier for many researchers.
• Integrating multiple scRNA-seq datasets, managing large-scale computations, and ensuring workflow reproducibility are significant challenges.

Purpose of the Study:

• To develop a user-friendly, web-based platform, scExplorer, to overcome computational barriers in scRNA-seq data analysis.
• To provide researchers, especially those without programming expertise, with tools for comprehensive and reproducible scRNA-seq analysis.

Main Methods:

• Implemented comprehensive batch correction using four algorithms: ComBat, Scanorama, BBKNN, and Harmony.
• Integrated SLURM-based job scheduling with pause/resume capabilities for efficient, large-scale analysis.
• Developed automated generation of publication-ready reports with exportable configuration files for full reproducibility.

Main Results:

• scExplorer successfully integrates multiple state-of-the-art batch correction algorithms.
• The platform supports scalable job management for datasets up to hundreds of thousands of cells.
• Automated reporting and configuration file export ensure transparency and reproducibility.

Conclusions:

• scExplorer democratizes scRNA-seq analysis by removing technical hurdles for non-computational researchers.
• The platform enhances collaborative research by providing transparent, reproducible, and accessible tools for complex data analysis.