一个视觉-omics基础模型,以弥合他的病理学与空间转录学
Weiqing Chen1,2, Pengzhi Zhang1,3,4,5, Tu N Tran1,3,4,5
1Center for Bioinformatics and Computational Biology, Houston Methodist Research Institute, Houston, TX, USA.
Nature methods
|May 29, 2025
概括
我们开发了OmiCLIP,这是一种新的视觉-奥米克基础模型,将组织组织学图像与转录组数据集成为先进的计算生物学. 我们的Loki平台能够准确地进行空间转录分析和基因表达预测.
科学领域:
- 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.
- 基因组学就是基因组学.
背景情况:
- 像单细胞RNA测序和空间转录组学这样的Omics技术可以生成详细的基因组数据.
- 当前的计算模型经常单独分析omics数据或图像数据,阻碍了综合分析.
- 整合组织学图像与转录基因数据对于全面了解生物系统至关重要.
研究的目的:
- 开发一个基础模型,OmiCLIP,将血素和素 (H&E) 染色组织图像与转录基因数据集成在一起.
- 创建基于OmiCLIP的Loki平台,提供一套用于视觉omics分析的工具.
- 为了使精确的空间转录学基因表达预测直接从H&E图像.
主要方法:
- 通过将Visium组织补丁中的H&E图像和转录组数据连接起来,开发了OmiCLIP,一种视觉-omics基础模型.
- 通过连接每块顶部表达的基因符号,将转录基因数据转化为"句子".
- 策划了一大数据集,包括220万个配对的组织图像和32个器官的转录组数据,用于模型训练.
- 构建了Loki平台的功能,包括组织对齐,注释,细胞类型分解和检索.
主要成果:
- 在大型数据集上训练的OmiCLIP成功地集成了组织学和转录学.
- 洛基平台在5个模拟和23个不同的数据集 (19个公共,4个内部) 中表现出一致的准确性和稳定性.
- 洛基在各种视觉omics任务中表现优于22个最先进的模型.
结论:
- OmiCLIP和Loki平台代表了在计算生物学中整合成像和omics数据的重大进步.
- 这种综合方法增强了空间转录学和基因表达的分析.
- 洛基为基因组学和生物信息学研究人员提供了一个强大而通用的工具.
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