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bioRxiv · 10.1101/2023.11.13.566851

ScRNAbox: Empowering Single-Cell RNA Sequencing on High Performance Computing Systems

Thomas, R. A.·Fiorini, M. R.·Amiri, S.·Fon, E. A.·Farhan, S. M. K.
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Abstract

MotivationSingle-cell RNA sequencing (scRNAseq) offers powerful insights, but the surge in sample sizes demands more computational power than local workstations can provide. Consequently, high-performance computing (HPC) systems have become imperative. Existing web apps designed to analyze scRNAseq data lack scalability and integration capabilities, while analysis packages demand coding expertise, hindering accessibility. ResultsIn response, we introduce scRNAbox, an innovative scRNAseq analysis pipeline meticulously crafted for HPC systems. This end-to-end solution, executed via the SLURM workload manager, efficiently processes raw data from standard and Hashtag samples. It incorporates quality control filtering, sample integration, clustering, cluster annotation tools, and facilitates cell type-specific differential gene expression analysis between two groups. ImplementationOpen-source code and comprehensive usage instructions with examples are available at https://neurobioinfo.github.io/scrnabox/site/. Supplementary InformationSupplementary data are available at Bioinformatics online.

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Thomas, R. A., Fiorini, M. R., Amiri, S., Fon, E. A., Farhan, S. M. K.. 2023-11-15. ScRNAbox: Empowering Single-Cell RNA Sequencing on High Performance Computing Systems. https://doi.org/10.1101/2023.11.13.566851

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