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相关概念视频

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
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Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards
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scViewer:一个交互式单细胞基因表达可视化工具.

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  • 1Global Statistical and Data Sciences, Teva Pharmaceuticals, West Chester, PA 19380, USA.

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概括
此摘要是机器生成的。

scViewer是一个新的R/Shiny应用程序,用于可视化单细胞RNA测序 (scRNA-seq) 数据. 它提供了对基因表达,差异表达和共同表达的用户友好的探索,以实现高效的数据解释.

关键词:
R 闪亮的闪亮的闪亮的生物信息学是一种生物信息学.共同表达是一种共同表达.不同的表达分析分析差异表达分析.基因表达的基因表达方式这就是 scRNA-seqq.一个单细胞RNA测序.

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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) 产生高维,复杂的数据,对于理解细胞多样性至关重要.
  • 现有的分析工具往往缺乏用于差异和共同表达分析的直观可视化功能.
  • 对scRNA-seq数据的有效解释对于生物发现至关重要.

研究的目的:

  • 介绍一下scViewer,这是一个交互式R/Shiny应用程序,用于可视化scRNA-seq数据.
  • 为研究人员提供一种易于使用的工具,用于探索基因表达,差异表达和共同表达.
  • 为了促进复杂的scRNA-seq数据集的解释,进行多条件比较.

主要方法:

  • 开发了scViewer作为一个交互式图形用户界面 (GUI) R/Shiny应用程序.
  • 使用已处理的Seurat RDS对象作为数据分析的输入.
  • 实施统计方法,包括负二项式混合建模用于微分表达式分析.

主要成果:

  • scViewer 便于可视化细胞类型特定的基因表达.
  • 该应用程序允许对基因对进行共同表达分析.
  • 它执行差异表达分析,考虑细胞层面和主体层面的变化.
  • 为详细的数据解释,生成准备发布的图表.

结论:

  • scViewer提供了有效和用户友好的scRNA-seq数据的可视化.
  • 该工具支持即时基因水平差异和协同表达分析.
  • scViewer增强了生物信息学家和湿实验室科学家之间的合作.
  • 它是解释复杂的单细胞基因表达数据的宝贵资源.