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相关概念视频

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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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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DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
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共识标签与图形卷积网络的传播,用于单细胞RNA测序细胞类型的注释.

Daniel P Lewinsohn1,2, Katinka A Vigh-Conrad1, Donald F Conrad1

  • 1Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, United States.

Bioinformatics (Oxford, England)
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概括

我们开发了一个图形卷积网络 (GCN),用于自动化单细胞RNA测序 (scRNA-seq) 数据的细胞类型注释. 这种方法提高了准确性,并为细胞分类提供特征解释.

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科学领域:

  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 能够在细胞水平上进行基因表达分析.
  • 手动的细胞类型注释是劳动密集型的,容易主观.

研究的目的:

  • 为scRNA-seq数据的细胞类型注释开发一种自动化和可解释的方法.
  • 提高细胞类型分类的准确性和效率.

主要方法:

  • 使用图形卷积网络 (GCN) 进行半监督学习.
  • 从最先进的工具中进行集成的共识标签,以识别可靠的细胞标签.
  • 应用GCN在细胞图中传播自信标签.

主要成果:

  • 与共识算法和个人工具相比,基于GCN的方法显示了更好的准确性.
  • 取得了与非参数邻近多数方法相似的结果.
  • 通过识别细胞类型分类的关键基因,启用特征解释.

结论:

  • 开发的GCN管道为自动化和可解释的scRNA-seq数据分类提供了一个端到端的解决方案.
  • 这种方法提供了一个强大的,高效的替代手动注释.