DGSIST:基于深度图形结构的空间转录组数据集群 Infomax
Yu-Han Xiu1, Si-Lin Sun1, Bing-Wei Zhou1
1College of Information Science Technology, Hainan Normal University, HaiKou City 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, HaiKou City 571158, China.
Methods (San Diego, Calif.)
|October 16, 2024
概括
深度图形结构Infomax (DGSI) 模型和DGSIST框架利用空间转录学数据进行准确的细胞聚类和空间域识别. 这种无监督的方法增强了对组织组织和疾病结构的理解.
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:
- 空间转录学提供了对组织基因表达和结构的洞察力,但往往不充分利用空间数据.
- 图形神经网络提供了一个将空间信息与基因表达数据集成的机会.
- 现有的方法可能无法充分利用可用于转录数据集的丰富空间环境.
研究的目的:
- 开发一个无监督模型,DGSI (深度图形结构Infomax),用于从空间转录组学处理图形数据.
- 引入DGSIST框架,将DGSI与尺寸缩小和聚类集成在一起,以准确识别细胞类型.
- 加强空间转录学数据的分析,改进细胞聚类和空间域识别.
主要方法:
- 使用图形卷积神经网络和无监督学习方法开发了DGSI模型,以最大限度地提高图形和节点表示之间的相互信息.
- 集成的DGSI与Singular Value Decomposition (SVD) 和k-means++用于DGSIST无监督细胞聚类框架.
- 将DGSIST应用于不同组织类型和技术的各种空间转录组数据集.
主要成果:
- DGSIST准确地识别了细胞类型和空间域,性能优于现有的方法.
- 该框架有效地消除了批量效应,没有明确的纠正.
- 在各种组织类型和技术平台上表现出强大的性能.
结论:
- DGSIST是一种强大的无监督框架,用于细胞聚类和使用空间转录学数据进行空间分析.
- 该模型有效地捕获本地空间信息,从而提高了识别细胞结构的准确性.
- 在癌症等疾病中,DGSIST具有显著的潜力,可以促进对空间组织的理解.
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