准确预期最接近邻居G的完整空间随机性:一种可扩展的变换替代方案
bioRxiv : the preprint server for biology
|July 16, 2025
概括
研究人员开发了一种快速,无变的方法来计算生物数据的空间随机性. 这提高了空间转录学和流行病学等领域的可复制性.
科学领域:
- 空间生物学 空间生物学
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
背景情况:
- 诸如近邻G (r) 等空间指标至关重要,但由于生物系统的非静止性,它们经常被滥用.
- 目前的方法依赖于计算上昂贵的排列来估计完全的空间随机性 (CSR).
研究的目的:
- 引入封闭形式的分析解决方案,用于最接近邻居G的样本特定的CSR.
- 为了实现快速的,可重复的计算,而没有变换,提高空间分析的效率.
主要方法:
- 对于最接近邻居G的样本特定CSR的平均值和差异的衍生分析解决方案.
- 使用模拟数据和清细胞细胞癌的多重免疫光样本验证了该方法.
- 将计算性能与基于排列的方法进行比较.
主要成果:
- 对于平均值G(r) 的分析解决方案与模拟数据上的顺序结果相匹配.
- 理论G (r) 显示了实际数据的差异,高估了低半径的CSR,低估了中等范围的半径.
- Rcpp的实现速度是~30倍快,并且使用的内存比1000次排列减少了~20倍.
结论:
- 无位的分析方法显著提高了空间分析的计算效率和可重复性.
- 这种方法对于在流行病学,多重免疫光学和空间转录学中准确的样本特定的零期望至关重要.
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