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

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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

Longitudinal formative assessment reforms in manual therapy education: impacts on psychological wellbeing and course satisfaction.

Frontiers in medicine·2026
Same author

Costunolide ameliorates autoimmune uveitis by targeting USP15 to suppress TNF-α-induced retinal endothelial inflammation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Multi-Omics Analysis Reveals the Gut-Mediated Mechanism Underlying the Seasonal Non-Laying Phenotype in Zhedong White Geese (<i>Anser cygnoides domesticus</i>).

Animals : an open access journal from MDPI·2026
Same author

Chloroplast photorespiratory bypass in tomato couples carbon-nitrogen assimilation to increase yield and fruit quality.

Cell reports·2026
Same author

Multifunctional nanotherapeutics for targeted modulation of endoplasmic reticulum stress to potentiate cancer therapy.

Asian journal of pharmaceutical sciences·2026
Same author

Forsythiaside A Alleviates LPS-Induced Mastitis by Inhibiting Ferroptosis and Oxidative Stress.

Animals : an open access journal from MDPI·2026

相关实验视频

Updated: May 13, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

深度学习驱动的单细胞集群框架具有更高的准确性和稳定性.

Yi Zhang1,2, Xi Feng3,4, Yin Wang1,2

  • 1Guilin University of Technology, Guilin, 541004, China.

Scientific reports
|February 3, 2025
PubMed
概括

scG-cluster通过将节点分布整合到图形集群中来增强单细胞RNA测序分析. 这种新的方法提高了细胞类型识别的准确性和可扩展性,优于现有的技术.

关键词:
注意力机制注意力机制细胞异质性 细胞异质性深度结构集群是指深度结构集群.标签: 美国 美国没有监督的集群.这就是 scRNA-seqq.

更多相关视频

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

471

相关实验视频

Last Updated: May 13, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.4K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

471

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) 对于理解细胞多样性至关重要.
  • 无监督的集群识别细胞类型,但基于图的方法往往忽视节点分布,并遭受过度平滑.
  • 现有的方法难以准确和可扩展的细胞群体表示.

研究的目的:

  • 引入scG-cluster,一种用于scRNA-seq数据的新型深度结构聚类方法.
  • 解决现有的基于图形的集群的局限性,包括节点分布疏忽和过度平滑.
  • 提高scRNA-seq分析中细胞类型识别的准确性和可扩展性.

主要方法:

  • 开发了scG集群,具有双拓邻近图,以结合节点分布.
  • 采用双拓自适应图形卷积网络 (TAGCN) 具有注意力和剩余连接.
  • 实施集群中心的代改进,以提高稳定性.

主要成果:

  • scG-cluster在六个不同的scRNA-seq数据集中展示了卓越的集群准确性和可扩展性.
  • 废除研究证实了注意力机制和残留连接的有效性.
  • 该方法始终超过了最先进的集群方法.

结论:

  • scG-cluster为scRNA-seq数据中的无监督聚类提供了强大的和有效的解决方案.
  • 双拓图形和TAGCN架构显著改善了细胞群体的表示和分化.
  • 拟议的方法推进了单细胞数据分析领域,提供了更准确和可扩展的细胞类型识别.