细胞组合的空间模式分析 (SPACE) 发现了细胞和组织层面的复杂空间组织
Edward C Schrom1, Erin F McCaffrey2, Vivek Sreejithkumar1
1Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA.
bioRxiv : the preprint server for biology
|January 3, 2024
概括
细胞组合的空间模式分析 (SPACE) 是一个新的R包,它分析高复杂生物数据中的复杂空间模式. 它识别和描述组织元素的排列,揭示了对生理和病理过程的洞察力.
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.
- 空间转录学 空间转录学
背景情况:
- 组织组织和细胞元素的空间模式对于生理和病理过程至关重要.
- 辨别组织中涉及多个生物元素的复杂空间模式是一个重大挑战.
- 现有的方法经常与高度复杂的空间数据的复杂性作斗争.
研究的目的:
- 介绍细胞组合的空间模式分析 (SPACE),这是一个用于分析高复杂空间数据的R包.
- 提供用于检测和描述各种空间模式的工具,包括共发生,上下文相关联,梯度和方向.
- 为了能够在单个图像和不同样本组中识别显著的模式.
主要方法:
- 空间利用信息理论框架来探索所有可能的组织元素组合 (单,双,三胞胎等). ) 的情况.
- 它根据空间模式的强度对合奏进行排名,区别于随机分类或样本组之间的差异.
- 该包与各种数据模式兼容,可在固定的空间坐标 (像素或voxels) 上记录值.
主要成果:
- 空间成功地检测和描述生物组织中复杂的空间模式.
- 使用与定义的基底真理的小鼠淋巴结图像的验证证明了SPACE的优势.
- 将其应用于各种数据集,包括瘤和颗粒瘤,揭示了新的空间组织模式.
结论:
- SPACE提供了一个强大而通用的框架来分析高度复杂的空间数据,揭示复杂的组织组织.
- 该R套件可方便对检测到的空间模式进行直观的解释,有助于生物发现.
- 空间增强了我们理解空间模式在各种生物环境中的健康和疾病中的作用的能力.
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