Jove
Visualize
联系我们

相关概念视频

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

5.6K
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.
5.6K

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

Peripheral blood immune profiling reveals key signatures in newly diagnosed NK/T cell lymphoma patients.

Theranostics·2026
Same author

A Dual-Gene Signature of PMAIP1 and GADD45A for Early Detection of Intrahepatic Cholangiocarcinoma in the Context of Primary Sclerosing Cholangitis.

International journal of molecular sciences·2026
Same author

Delocalized Electronic States: The High-Shell Nitrogen Effects on Metal-Nitrogen-Carbon Catalysts.

Journal of the American Chemical Society·2026
Same author

TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.

International journal of molecular sciences·2026
Same author

TUBB2A expression and its prognostic significance in hepatocellular carcinoma revealed by cholesterol-metabolism-related gene profiling.

Frontiers in molecular biosciences·2026
Same author

Dimerization of Organic Cations Triggered by Self-Interstitials Enhances the Stability and Carrier Lifetime of FAPbI<sub>3</sub> Perovskites.

Nano letters·2026
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

相关实验视频

Updated: Jun 24, 2025

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

9.4K

SCSMD:基于光谱矩阵分解的单细胞一致集群.

Ran Jia1, Ying-Zan Ren1, Po-Nian Li2

  • 1School of Mathematics and Statistics, Shandong University, Weihai 264209, Shandong, China.

Briefings in bioinformatics
|June 10, 2024
PubMed
概括
此摘要是机器生成的。

基于光谱矩阵分解 (SCSMD) 的单细胞一致集群通过整合多种方法来准确识别细胞类型和发育轨迹分析来改进单细胞RNA测序分析. 这种强大的方法增强了对不同数据集的聚类结果.

关键词:
细胞类型测定集群算法集群算法集群算法集群算法集群算法集群算法一个单细胞RNA-seqq.频谱聚类是指光谱聚类.

更多相关视频

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.1K
Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array
07:19

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

8.5K

相关实验视频

Last Updated: Jun 24, 2025

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects
07:37

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Published on: January 9, 2020

9.4K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.1K
Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array
07:19

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Published on: September 7, 2018

8.5K

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) 分析依赖于集群分析来理解细胞异质性.
  • 现有的集群算法通常在集群数量和分配方面产生不一致的结果.
  • 准确的细胞类型识别和对发育轨迹的理解在scRNA-seq.中至关重要.

研究的目的:

  • 引入一种新的,全面的集群方法,即基于光谱矩阵分解 (SCSMD) 的单细胞一致集群.
  • 提高scRNA-seq数据中集群识别的准确性和一致性.
  • 为了验证SCSMD在各种数据集和细胞类型中的性能.

主要方法:

  • 开发了基于光谱矩阵分解 (SCSMD) 的单细胞一致集群.
  • 集成多种集群方法来确定最佳的集群方案.
  • 使用15个真实的scRNA-seq数据集和一个定制的评估指标验证了SCSMD.

主要成果:

  • 在不同的scRNA-seq数据集中,SCSMD在集群数量和分配准确度方面表现出强的表现.
  • 应用于人类胚胎干细胞,SCSMD识别了已知的细胞类型和发育轨迹.
  • 在质母细胞瘤细胞中,SCSMD准确地检测出细胞类型,并在现有细胞群中提供了更细致的子集群.

结论:

  • 在scRNA-seq数据中,SCSMD提供了一种优越且一致的集群分析方法.
  • 该方法有效地识别了细胞类型,发育轨迹和更细致的细胞区别.
  • SCSMD可扩展到更大的数据集,并适合在scRNA-seq研究中进行进一步的下游分析.