单细胞多组学题嵌入揭示了细胞类型特定和COVID-19严重程度相关的免疫特征
Manqi Zhou1,2, Hao Zhang3, Zilong Bai2,3
1Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
Cell reports methods
|September 6, 2023
概括
我们开发了moETM,这是一种可解释的深度学习方法,用于分析复杂的单细胞多omics数据. 这种方法增强了对细胞异质性的理解,并确定了COVID-19等疾病的生物标志物.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞多组体测序使细胞异质性的全面分析成为可能.
- 多omics数据的高维度,离散和稀疏性质带来了重大的分析挑战.
研究的目的:
- 提出moETM,一种可解释的深度学习方法,用于对高维单细胞多式数据的综合分析.
- 为了证明moETM的优越性能与现有的最先进的方法相比.
主要方法:
- moETM使用专家产品编码器集成多个omics数据.
- 用多个线性解码器来学习多个omics的签名.
- 该方法在七个公开可用的数据集上进行了评估.
主要成果:
- 在整合性分析中,moETM的表现优于六种最先进的方法.
- 对scRNA + scATAC数据的应用确定了调节免疫基因的转录因子结合动机.
- 对来自COVID-19患者的CITE-seq数据的分析揭示了细胞类型特定的特征和关键条件的新型多omics生物标志物.
结论:
- moETM为单细胞多omics数据分析提供了一种强大且可解释的方法.
- 该方法为基因调节提供了宝贵的生物学见解,并为疾病生物标志物提供了临床见解.
关键词:
DL DL 是一个字.在 ETM ETM 中.在SM SM SM SM中,你会发现什么?这就是VAE的意义.深度学习是一种深度学习.嵌入的TOPIPC模型一个单细胞的多组组学.变量自动编码器变量自动编码器更多相关视频
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