ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems.

IF 2.9 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2024-10-01 DOI:10.1186/s12859-024-05935-y

Rhalena A Thomas, Michael R Fiorini, Saeid Amiri, Edward A Fon, Sali M K Farhan

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引用次数: 0

Abstract

Background: Single-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.

Results: In 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. We demonstrate the application of scRNAbox by analyzing two publicly available datasets.

Conclusion: ScRNAbox is a comprehensive end-to-end pipeline designed to streamline the processing and analysis of scRNAseq data. By responding to the pressing demand for a user-friendly, HPC solution, scRNAbox bridges the gap between the growing computational demands of scRNAseq analysis and the coding expertise required to meet them.

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SCRNAbox：在高性能计算系统上支持单细胞 RNA 测序。

背景：单细胞 RNA 测序（scRNAseq）提供了强大的洞察力，但样本量的激增要求更强的计算能力，而本地工作站无法提供。因此，高性能计算（HPC）系统势在必行。现有的用于分析 scRNAseq 数据的网络应用程序缺乏可扩展性和集成能力，而分析软件包需要专业的编码知识，这阻碍了其普及：为此，我们推出了 scRNAbox，这是一个专为高性能计算系统精心设计的创新型 scRNAseq 分析管道。这一端到端解决方案通过 SLURM 工作负载管理器执行，可高效处理来自标准样本和 Hashtag 样本的原始数据。它整合了质量控制过滤、样本整合、聚类、聚类注释工具，并促进了两组之间特定细胞类型的差异基因表达分析。我们通过分析两个公开数据集演示了 scRNAbox 的应用：ScRNAbox 是一个全面的端到端管道，旨在简化 scRNAseq 数据的处理和分析。scRNAbox 满足了人们对用户友好型高性能计算解决方案的迫切需求，在日益增长的 scRNAseq 分析计算需求与满足这些需求所需的编码专业知识之间架起了一座桥梁。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.