scReady - an automated and accessible pipeline for single-cell RNA-Seq preprocessing: Empowering novice bioinformaticians

<ns3:p>The widespread adoption of single-cell RNA sequencing (scRNAseq) has democratized high-resolution transcriptomic analysis. Although data generation remains costly, many publicly available datasets can be leveraged to address focused research questions. However, the complexity of data processing remains a significant barrier for new bioinformaticians or laboratories without dedicated bioinformatics support. While best practices for scRNAseq analysis have been proposed, their implementation often requires extensive tool installation, testing, and customization. To address these challenges, we developed scReady, a containerized pipeline that automates and standardizes preprocessing for scRNA-seq data. Designed for both single machines and high-performance computing clusters, the pipeline ensures flexibility, scalability, and reproducibility. It integrates essential quality control steps, including ambient RNA removal, doublet detection, and cell/gene filtering based on mitochondrial content and customizable thresholds. It also handles CITE-seq data (ADT and HTO). Following normalization and feature selection, scReady outputs a fully processed Seurat object along with diagnostic plots and a comprehensive quality control report. In a representative use case, we applied scReady to integrate synovial tissue scRNA-seq datasets generated using different digestion protocols, starting from Cell Ranger–produced count matrices. With a single command, scReady performed all preprocessing steps and generated integrated UMAP visualizations and quality control metrics consistent with published results. This example illustrates how scReady reduces the coding barrier to reproducible, standardized preprocessing of complex single-cell datasets. By handling the technical execution, scReady empowers researchers to focus their expertise on the critical tasks of parameter selection based on their experimental context and the subsequent biological interpretation.</ns3:p>