发表机构
Brown University; Bryn Mawr College; University of Maryland; Michigan State University; University of Illinois Urbana-Champaign（布朗大学; 布林莫尔学院; 马里兰大学; 密歇根州立大学; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
COMPASS是一个基于深度学习的彗星试验图像分析自动化流程，结合分割、损伤测量和自动选择，提供标准化测量并提高可重现性。
AI 中文摘要
摘要：单细胞凝胶电泳（“彗星”）试验是一种广泛用于在单个细胞水平上量化DNA损伤的技术。然而，图像分析通常依赖于人工检查或半自动化软件，这可能劳动强度大、难以重现，并且对图像质量和彗星形态敏感。COMPASS通过将基于深度学习的彗星分割与损伤测量和自动彗星选择相结合，实现了彗星试验图像分析的自动化。该流程产生标准化的DNA损伤测量结果，同时提供稳健的检测，减少人工工作量，并通过透明的选择和可选的人工审查提高可重现性。可用性和实现：COMPASS使用Python实现，并可在https://this https URL rsinghlab/COMPASS免费获取。安装说明、预训练权重和示例用法在存储库中提供。联系方式：jack_roberts2@brown.edu, ritsingh@illinois.edu 补充信息：出版后可在线获取。
英文摘要
Summary: The single-cell gel electrophoresis ('comet') assay is a widely used technique for quantifying DNA damage at the individual cell level. However, image analysis often relies on manual inspection or semi-automated software, which can be labor-intensive, difficult to reproduce, and sensitive to image quality and comet morphology. COMPASS automates comet assay image analysis by combining deep learning-based comet segmentation with damage measurement and automated comet selection. The pipeline produces standardized DNA damage measurements while offering robust detection, reducing manual effort and improving reproducibility through transparent selection and optional manual review. Availability and implementation: COMPASS is implemented in Python and is freely available at https://github.com/ rsinghlab/COMPASS. Installation instructions, pretrained weights, and example usage are provided in the repository. Contact: jack_roberts2@brown.edu, ritsingh@illinois.edu Supplementary information: Available onlime upon publication.
