对空间分子数据进行强大而准确的病例控制分析,使用深度学习定义的组织微区

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Yakir Reshef^1,2,3,4, Lakshay Sood^1,2,3,4, Michelle Curtis^1,2,3,4

¹Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA.

bioRxiv : the preprint server for biology

|February 20, 2025

概括

新方法VIMA使用深度学习来找到空间疾病特征. 它识别了与阿尔茨海默病,性结肠炎和类风湿性关节炎相关的生物微观,改善了疾病分析.

科学领域:

计算生物学和生物信息学
空间转录学和分子病理学

背景情况:

越来越多的空间分子数据需要用于疾病相关结构识别的先进方法.
目前依赖于手动注释的方法可能会错过关键的生物信号.

研究的目的:

引入基于变异推理的微观分析 (VIMA),以灵活精确地发现空间疾病特征.
开发深度学习和统计方法,以克服现有方法的局限性.

主要方法:

维玛采用一个变化自编码器,从组织补丁中生成数值"指纹".
这些指纹在样本中定义了"microniches" - - 生物学上相似的组织群.
严格的统计数据确定了与病例控制状态相关的微利.

主要成果:

与其他方法相比,VIMA在模拟中展示了优越的校准,功率和准确性.
适用于阿尔茨海默氏症痴呆症,性结肠炎 (UC) 和类风湿性关节炎 (RA) 数据集,VIMA汇总了已知的生物学.
在所有测试的数据集中发现了新的空间疾病特征.

结论:

维玛提供了一种强大,数据驱动的方法来揭示疾病的空间特征.
该方法增强了用于生物发现的复杂空间分子数据的分析.

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