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

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

Optimal gene panel selection for targeted spatial transcriptomics experiments.

Nucleic acids research·2026
Same author

Learning External Point-Set Context for Point Cloud Segmentation.

IEEE transactions on neural networks and learning systems·2026
Same author

Cavity-Reconstructed Exciton Relaxation and Charge Transfer in WS<sub>2</sub>/MoS<sub>2</sub> Junctions.

Journal of the American Chemical Society·2026
Same author

Correction: Metabolome and transcriptome integration reveals cerebral cortical metabolic profiles in rats with subarachnoid hemorrhage.

Frontiers in aging neuroscience·2026
Same author

Dynamic lattice disorder overrides energetics for barrierless interfacial charge transfer in 2D hybrid perovskites.

Science advances·2026
Same author

Caffeine-mediated CD39<sup>+</sup> Treg activation via the CD39-adenosine receptor pathway is a novel risk factor for pulmonary tuberculosis.

Frontiers in immunology·2026

相关实验视频

Updated: Jun 27, 2025

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.4K

在单单细胞数据中进行正交叉多模式集成和集群.

Yufang Liu1, Yongkai Chen1, Haoran Lu1

  • 1Department of Statistics, University of Georgia, Athens, GA, 30602, USA.

BMC bioinformatics
|April 25, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了用于分析CITE-seq数据的正交叉多模式集成和集群 (OMIC). 这种新的方法有效地整合了多式联运数据,在细胞聚类准确性和效率方面超过了现有的方法.

关键词:
在CITE-seqq.细胞聚类是细胞的聚类.多式联运的整合是多式联运.

更多相关视频

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
A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

9.7K

相关实验视频

Last Updated: Jun 27, 2025

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
11:19

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Published on: March 20, 2018

10.4K
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
A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

9.7K

科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 数据科学是数据科学.

背景情况:

  • 多式联运集成将多种数据源结合起来,以获得更深入的见解.
  • 分析多学科数据面临复杂性,高维度和异质性方面的挑战.
  • 复杂的计算工具对于解释和可视化多omics数据至关重要.

研究的目的:

  • 引入一种新的方法,即直角多模式集成和聚类 (OMIC),用于分析CITE-seq数据.
  • 使研究人员能够整合多个信息来源,同时考虑它们的依赖性.
  • 用CITE-seq数据集来证明OMIC在细胞聚类方面的有效性.

主要方法:

  • 开发了正交多模式集成和聚类 (OMIC) 方法.
  • 将OMIC应用于CITE-seq数据集以实现多式联运数据集成.
  • 对OMIC与现有的细胞聚类方法进行比较分析.

主要成果:

  • OMIC有效地整合了来自CITE-seq数据的多个信息来源.
  • 与现有方法相比,拟议的OMIC方法在细胞聚类方面表现出更高的准确性.
  • 在多式联网数据分析中,OMIC 提供了更高的计算效率和可解释性.

结论:

  • OMIC 方法为多式联运数据分析提供了强大而可靠的工具.
  • OMIC提高了综合数据解释的可行性和准确性.
  • 这种方法推进了多主题数据分析和CITE-seq解释领域.