扫地py:Python生态系统中的空间omics数据的空间意识质量控制指标
Xingyi Chen1, Michael Totty2, Stephanie C Hicks2,3,4,5,6,7
1Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, MD, USA.
bioRxiv : the preprint server for biology
|December 22, 2025
概括
SpotSweeper-py为Python中的空间转录学数据提供了空间意识的质量控制. 该工具通过识别本地文物而提高数据可靠性,而不会去除生物学相关的组织区域.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 空间分辨率转录学 (SRT) 生成复杂的数据集.
- 全球质量控制 (QC) 指标可以不准确地删除生物信号或错过SRT数据中的本地化文物.
- 现有的空间感知质量控制工具仅限于R编程语言,阻碍了与Python/scverse生态系统的集成.
研究的目的:
- 介绍SpotSweeper-py,这是一个Python包,为SRT数据提供社区意识的QC指标.
- 为了使先进的本地QC在Python/scverse环境中无集成.
- 通过减少假阳性和保存组织架构来提高SRT数据分析的准确性和可靠性.
主要方法:
- 开发SpotSweeper-py,这是一个Python软件包,用于QC指标实现社区意识的z-score.
- 对总计数,日志总计数,检测到的基因和线粒体百分比的z-score的计算.
- 在10x基因组学Visium和VisiumHD数据集上展示SpotSweeper-py的性能.
- 包含用于异常值可视化的绘图实用程序.
主要成果:
- SpotSweeper-py有效计算本地,空间意识的质量控制指标.
- 该软件包可以顺利地与Python/scverse生态系统集成.
- 它成功地减少了全球QC的假阳性,同时保留了组织特异性结构.
- 在公开的Visium和VisiumHD数据集上验证了性能.
结论:
- SpotSweeper-py提供了一个强大的,基于Python的解决方案,用于SRT分析中的本地QC.
- 该工具提高了SRT数据处理管道的可靠性.
- 它使先进的,空间意识的QC可供更广泛的研究人员使用.
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