Abstract

Summary Computational analysis of single-cell RNA sequencing (scRNA-seq) data presents significant barriers for researchers lacking programming expertise, particularly for multi-dataset integration, scalable job management, and reproducible workflows. We developed scExplorer, a web-based platform that addresses these limitations through three key innovations: Comprehensive batch correction using four state-of-the-art algorithms (ComBat, Scanorama, BBKNN, and Harmony), SLURM-based job scheduling with pause/resume functionality for large-scale analyses, and automated generation of publication-ready reports with exportable configuration files ensuring complete reproducibility. The platform's modular Docker architecture supports both standalone and client-server deployments, enabling analysis of datasets ranging from thousands to hundreds of thousands of cells. An openly documented REST API clarifies how the interface orchestrates analyses and supports transparent operation. scExplorer eliminates the technical barriers that prevent non-computational researchers from performing rigorous scRNA-seq analysis while maintaining the transparency and reproducibility standards required for collaborative research. Availability and implementation https://apps.cienciavida.org/scexplorer/. © 2025 The Author(s). Published by Oxford University Press.