操作行为表型化的自动管道,用于高通量数据管理,处理和可视化
Sunwoo Kim1, Yunyi Huang1, Uday Singla1
1Department of Psychiatry, University of California - San Diego, La Jolla, CA, 92093, USA.
NPP - digital psychiatry and neuroscience
|December 8, 2025
概括
这项研究开发了一个用于临床前成研究的自动化数据管道,有效地从操作任务中处理大型行为数据集. 这提高了物质使用障碍研究的高通量表型,提高了数据质量和可访问性.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 行为科学 行为科学
背景情况:
- 操作行为范式对于神经精神疾病的临床前模型至关重要,产生了广泛的时间数据.
- 全基因组关联研究 (GWAS) 需要对诸如上之类的复杂行为进行强有力的表型化.
- 临床前成研究联盟 (PARC) 为成研究从3000只老鼠中生成了超过10万个数据文件.
研究的目的:
- 开发一个自动化,高效的数据处理管道,用于大规模的操作者行为研究.
- 管理,存储和处理用于GWAS和其他分析的行为数据.
- 在临床前成研究中提高数据质量,可访问性和可重复性.
主要方法:
- 利用云服务 (Dropbox,微软Azure) 和自动化数据处理的自定义脚本.
- 将原始操作器输出文件转换为结构化的Excel,并与元数据集成到关系SQL数据库中.
- 实现自动化质量控制,数据备份,汇总报告和交互式可视化.
主要成果:
- 开发了一个强大的,自动化管道,用于处理超过10万个行为数据文件.
- 显著减少了人工工作量和错误,提高了数据质量和可访问性.
- 实现了有效的高通量表型化,用于物质使用障碍研究.
结论:
- 自动化数据处理管道简化了操作研究,提高了效率,透明度和可重复性.
- 这种工作流是可扩展的,对任何规模的临床前研究都有好处.
- 通过改善数据可访问性和管理,促进合作研究.
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