Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

RNA-seq03:21

RNA-seq

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 microarray-based...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Continuous Size-Based Particle Separation Using Inertial Force and Deterministic Lateral Displacement.

Micromachines·2026
Same author

Interfacial Energy Balance Governs Initial Cell Spreading Dynamics.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

Electroactive Asymmetric Dressing for Spatiotemporal Deep Burn Scarless Healing and Management.

Advanced healthcare materials·2025
Same author

Extended replicative lifespan of primary resting T cells by CRISPR/dCas9-based epigenetic modifiers and transcriptional activators.

Cellular and molecular life sciences : CMLS·2024
Same author

Mechanical Constraints in Tumor Guide Emergent Spatial Patterns of Glioblastoma Cancer Stem Cells.

Mechanobiology in medicine·2024
Same author

Recent progress of organ-on-a-chip towards cardiovascular diseases: advanced design, fabrication, and applications.

Biofabrication·2023

相关实验视频

Updated: Jul 6, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.4K

分裂:使用分解网络从空间解析的转录组学数据中准确而强大的揭示细胞景观.

Zhongning Jiang1, Wei Huang1, Raymond H W Lam2,3

  • 1Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China.

BMC bioinformatics
|December 19, 2024
PubMed
概括

新的计算方法Spall通过整合单细胞RNA测序 (scRNA-seq) 和空间解析的转录组学 (SRT) 数据,准确地绘制组织中的细胞类型. 它有效地平衡空间模式与细胞特征,改善组织结构分析.

关键词:
细胞类型的比例比例.分解分解是指分解.图表神经网络的神经网络空间分辨的转录学.组织结构组织结构.

更多相关视频

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants
08:33

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

8.0K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K

相关实验视频

Last Updated: Jul 6, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.4K
Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants
08:33

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

8.0K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K

科学领域:

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

背景情况:

  • 空间解析转录学 (SRT) 推进了组织结构的表征.
  • 现有的分解方法难以平衡空间连续性与细胞特异性数据保存.
  • 准确的细胞分布推断对于理解组织组织至关重要.

研究的目的:

  • 开发一种新的分解网络,Spall,用于准确的细胞类型比例推断.
  • 为了有效地整合单细胞RNA测序 (scRNA-seq) 和SRT数据.
  • 克服现有方法在平衡空间和细胞特征方面的局限性.

主要方法:

  • 提出了Spall,一个新的分解网络,集成scRNA-seq和SRT数据.
  • 引入了带有动态注意力机制的GATv2模块,以捕捉现场关系.
  • 包含跳过连接来保存细胞特异信息,有助于预测罕见细胞类型.

主要成果:

  • 在重建跨多个数据集的细胞分布模式方面,Spall优于最先进的方法.
  • 在人类胰腺管腺癌中成功揭示了瘤异质性.
  • 描绘出复杂的组织结构,包括小脑和大脑皮层.

结论:

  • 斯帕尔精确推断细胞类型比例,并改善空间分析.
  • 为下游分析提供可靠的低维嵌入.
  • 为解读复杂的组织结构和异质性提供了新的途径.