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相关概念视频

RNA-seq03:21

RNA-seq

9.8K
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...
9.8K
Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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相关实验视频

Updated: May 25, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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通过基因表达和数据驱动的基因-基因相互作用集成增强单细胞RNA-seq嵌入.

Hojjat Torabi Goudarzi1, Maziyar Baran Pouyan2

  • 1Electrical Engineering and Computer Science Department, Oregon State University, Address one, Corvallis, 97331, OR, United States.

Computers in biology and medicine
|February 25, 2025
PubMed
概括

这项研究引入了一种用于单细胞RNA测序 (scRNA-seq) 分析的新方法,该方法集成了基因表达和基因相互作用. 这种方法改善了罕见细胞种群的识别,并加强了下游分析,以更好地了解细胞多样性.

关键词:
细胞嵌入 细胞嵌入数据驱动的基因-基因相互作用.基因表达 基因表达 基因表达图表神经网络的神经网络类似性学习的学习.一个单细胞RNA-seqq.

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相关实验视频

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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) 提供了对细胞异质性的高分辨率见解.
  • 由于高维度和技术噪音,scRNA-seq数据存在分析挑战.
  • 现有的嵌入方法往往忽视了对细胞身份至关重要的基因相互作用.

研究的目的:

  • 为scRNA-seq数据开发一种新的嵌入方法,该方法集成了基因表达特征和基因-基因相互作用.
  • 通过结合监管关系,创造一个更全面的细胞状态的表现.
  • 提高罕见细胞群的检测和下游分析性能.

主要方法:

  • 使用随机森林模型构建细胞-叶子图 (CLG),以捕捉基因调节关系.
  • 构建了一个K-近邻图 (KNNG) 来表示细胞表达相似性.
  • 将CLG和KNNG组合成一个丰富的细胞叶图 (ECLG),用于基于图形神经网络的细胞嵌入.

主要成果:

  • 拟议的方法通过整合表达水平和基因-基因相互作用,提供了更全面的细胞嵌入.
  • 在多个数据集中证明了罕见细胞种群的增强检测.
  • 在下游分析中表现得更好,包括可视化,集群和轨迹推断.

结论:

  • 这种新的嵌入方法在scRNA-seq数据分析方面取得了重大进展.
  • 整合基因表达和基因对基因相互作用为研究细胞多样性和动态提供了更完整的框架.
  • 这种方法在单细胞分辨率下增强了复杂生物系统的分析能力.