surviveR:一个灵活的光亮的应用程序,用于分析患者的生存率
Tamas Sessler1, Gerard P Quinn1, Mark Wappett1
1Patrick G. Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
Scientific reports
|December 12, 2023
概括
surviveR是一款基于云的新工具,为研究人员简化了复杂的生存分析. 它使得简单的队列生成和基因组数据的分析成为可能,使得高级的卡普兰-梅尔 (KM) 存活图对非专家也可用.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组数据分析 基因组数据分析
背景情况:
- 卡普兰-梅尔 (KM) 存活分析对生物医学研究至关重要,尤其是在越来越复杂的患者数据方面.
- 现有的商业和开源工具存在局限性,包括功能限制或高计算障碍.
- 对于具有有限计算专业知识的研究人员来说,需要可访问的工具来促进复杂的生存分析.
研究的目的:
- 开发一个易于使用,基于云的工具,用于执行复杂的生存分析.
- 为了使具有有限计算专业知识的研究人员能够进行常规但复杂的分析.
- 为队列生成和生存数据绘制提供综合定制.
主要方法:
- 开发 surviveR,一个基于云的 Shiny 应用程序.
- 整合了用于患者队列过和自定义组生成的功能.
- 实施自动化日志等级测试和考克斯危险比率计算.
- 支持连续数据集 (例如,RNA,蛋白质表达) 用于生存分类.
主要成果:
- surviveR提供了一个可访问的基于Web的平台,用于生存数据分析.
- 用户可以轻松生成定制的患者队列,并执行生存策划.
- 该工具有助于计算关键的生存指标,如日志等级p值和危险比率.
- 用于结直肠癌患者数据集的证明实用性.
结论:
- surviveR解决了对直观生存分析工具的未满足需求.
- 它使非专家用户能够使用各种数据集进行复杂的生存分析.
- 该应用程序使生物医学研究中的高级生存分析民主化.
相关概念视频
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