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

10.1K
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...
10.1K

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

Green emitting diatomite-derived Zn<sub>2</sub>SiO<sub>4</sub>:Mn as a dual functional sensor for the development of latent fingerprints.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Development of a cholera toxin B subunit-adjuvanted Helicobacter pylori multi-epitope vaccine and evaluation of its immunization effect in mice.

Cytokine·2026
Same author

Propagating Cross-View Semantics for Multi-view Clustering: A Unified Anchor Refinement Paradigm.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Associations between diet quality and multiple chemical exposures.

Ecotoxicology and environmental safety·2026
Same author

EyeRAG: graph retrieval-augmented generation for safe and accurate clinical dialogue in ophthalmology.

NPJ digital medicine·2026
Same author

Efficacy and safety of doravirine/lamivudine/tenofovir as initial treatment for people living with HIV in China.

Antimicrobial agents and chemotherapy·2026

相关实验视频

Updated: Jul 27, 2025

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
05:59

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

753

scDFC:用于单细胞RNA-seq数据的深度融合聚类方法.

Dayu Hu1, Ke Liang1, Sihang Zhou1

  • 1School of Computer, National University of Defense Technology, No. 109 Deya Road, 410073 Changsha, Hunan, China.

Briefings in bioinformatics
|June 6, 2023
PubMed
概括

这项研究引入了一种新的深度融合聚类模型,用于单细胞RNA测序数据,以更好地理解瘤异质性. 该方法有效地整合了属性和结构信息,以改进细胞亚群分析.

科学领域:

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

背景情况:

  • 聚类对于分析单细胞RNA测序 (scRNA-seq) 数据中的瘤异质性至关重要.
  • 传统的聚类方法在高维sscRNA-seq数据方面存在困难.
  • 深度聚类方法有希望,但往往忽视了同时整合细胞属性和结构信息.

研究的目的:

  • 为scRNA-seq数据开发一种新的深度融合聚类模型.
  • 有效地整合属性和结构信息,以提高聚类性能.
  • 改进细胞亚群和瘤微环境的研究.

主要方法:

  • 提出了一个单细胞深度聚变聚类模型,有两个模块:归因特征聚类和结构注意力特征聚类.
  • 利用两个自动编码器来处理各种特征类型.
  • 综合属性,结构和注意力信息,用于全面分析.

主要成果:

  • 拟议的模型证明了融合属性,结构和注意力信息的有效性和效率.
  • 实验结果证实了该模型在scRNA-seq数据上的有效性.
  • 这种方法有助于对细胞异质性的更细致的理解.
关键词:
聚类集群是指聚类的聚类.深度学习是一种深度学习.融合网络 融合网络 融合网络单细胞转录学 转录学

更多相关视频

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
09:09

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Published on: December 9, 2022

5.9K
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

18.6K

相关实验视频

Last Updated: Jul 27, 2025

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
05:59

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

753
Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics
09:09

Isolation of Nuclei from Flash-Frozen Liver Tissue for Single-Cell Multiomics

Published on: December 9, 2022

5.9K
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

18.6K

结论:

  • 新的深度融合集群模型为scRNA-seq数据分析提供了一个强大的工具.
  • 这种方法提高了识别细胞亚群和研究瘤微环境的能力.
  • 开源实现有助于进一步的研究和应用.