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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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GatorSC:多尺度细胞和基因图形与混合专家融合为单细胞转录组学.

Yuxi Liu1, Zhenhao Zhang2, Mufan Qiu3

  • 1Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, 46202, IN, USA.

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概括

GatorSC是单细胞RNA测序 (scRNA-seq) 数据分析的新框架. 它有效地融合了多尺度的细胞和基因图,提供了强大的,耐噪声的,低维的表示,改善了下游任务,如细胞聚类和注释.

关键词:
细胞聚类是细胞的聚类.细胞类型的注释.相反的学习学习.基因表达的归因 基因表达的归因专家的混合-专家的混合在 scRNA-seq 数据中.

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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RNA测序 (scRNA-seq) 提供了高分辨率的细胞异质性见解.
  • 现有的方法未充分利用scRNA-seq数据中的丰富结构信息,特别是由于噪音和稀疏性.
  • 整合基于细胞和基因的异质图形视图对于强大的低维表示至关重要.

研究的目的:

  • 介绍GatorSC,一个用于scRNA-seq数据的统一表示学习框架.
  • 利用多尺度的细胞和基因图表来增强信息融合.
  • 使用自主监督学习开发耐噪声和结构保护嵌入式.

主要方法:

  • GatorSC使用全球细胞-细胞,全球基因-基因和本地基因-基因图形来建模scRNA-seq数据.
  • 混合专家架构通过一个门网通过自适应性融合图形神经网络嵌入.
  • 一个统一的自我监督目标结合了图形重建和对比学习,用于细胞和基因图形.

主要成果:

  • 在19个不同的scRNA-seq数据集上评估了GatorSC.
  • 在14个基准数据集中,在细胞聚类,基因表达赋值和细胞类型注释方面超越了最先进的方法.
  • 在阿尔茨海默病数据集中证明了准确的轨迹推断和生物特征的恢复.

结论:

  • GatorSC为全面的单细胞转录组分析提供了灵活而强大的基础.
  • 该框架有效地整合了多尺度图形结构,以实现强大的表示学习.
  • GatorSC的方法可以扩展到多原子和空间转录原子数据.