Chatterjee Ranojoy, Gohel Chiraag, Shook Brett A, Rahnavard Ali
Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052.
Department of Biochemistry and Molecular Medicine, School of Medicine and Health Sciences, The George Washington University, Washington, DC 20052.
bioRxiv. 2025 May 22:2025.05.16.654572. doi: 10.1101/2025.05.16.654572.
Single-cell analysis has transformed our understanding of cellular diversity, offering insights into complex biological systems. Yet, manual data processing in single-cell studies poses challenges, including inefficiency, human error, and limited scalability. To address these issues, we propose the automated workflow , which integrates high-throughput sequencing in a user-friendly platform. By automating tasks like cell type clustering, feature extraction, and data normalization, reduces researcher workload, promoting focus on data interpretation and hypothesis generation. Its standardized analysis pipelines and quality control metrics enhance reproducibility, enabling collaboration across studies. Moreover, 's adaptability supports integration with emerging technologies, keeping pace with advancements in single-cell genomics. accelerates discoveries in single-cell biology, driving impactful insights and clinical translation. It is available with documentation and tutorials at https://github.com/omicsEye/cellSight.
单细胞分析改变了我们对细胞多样性的理解,为复杂生物系统提供了见解。然而,单细胞研究中的手动数据处理带来了挑战,包括效率低下、人为错误和可扩展性有限。为了解决这些问题,我们提出了自动化工作流程,该流程在用户友好的平台中集成了高通量测序。通过自动执行细胞类型聚类、特征提取和数据归一化等任务,减少了研究人员的工作量,促进了对数据解释和假设生成的关注。其标准化的分析管道和质量控制指标提高了可重复性,实现了跨研究的协作。此外,它的适应性支持与新兴技术集成,跟上单细胞基因组学的发展。它加速了单细胞生物学的发现,推动了有影响力的见解和临床转化。可在https://github.com/omicsEye/cellSight上获取文档和教程。