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

VizDefender: Unmasking Visualization Tampering Through Proactive Localization and Intent Inference.

IEEE transactions on visualization and computer graphics·2026
Same author

VizQStudio: Iterative Visualization Literacy MCQs Design with Simulated Students.

IEEE transactions on visualization and computer graphics·2026
Same author

Follow the Signs or the Crowd? Effects of Environmental Load and Crowd Dynamics in VR Evacuation.

IEEE transactions on visualization and computer graphics·2026
Same author

SceneLoom: Communicating Data with Scene Context.

IEEE transactions on visualization and computer graphics·2025
Same author

CellScout: Visual Analytics for Mining Biomarkers in Cell State Discovery.

IEEE transactions on visualization and computer graphics·2025
Same author

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials.

IEEE transactions on visualization and computer graphics·2025
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
Same journal

Spatial-temporal Relation guided Motion Transfer via Diffusion Model.

IEEE transactions on visualization and computer graphics·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
10:41

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Published on: December 16, 2015

9.3K

TrajLens:在交叉样本探索中构建细胞发育轨迹的视觉分析.

Qipeng Wang, Shaolun Ruan, Rui Sheng

    IEEE transactions on visualization and computer graphics
    |November 21, 2025
    PubMed
    概括
    此摘要是机器生成的。

    研究人员开发了一种基于GNN的模型和TrajLens视觉分析系统,用于预测和探索跨样本细胞发育轨迹,简化了单细胞RNA测序 (scRNA-seq) 中细胞空间动态的分析.

    更多相关视频

    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

    19.0K
    Analysis of Multidimensional Microscopy Data Using Cell-ACDC
    06:17

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC

    Published on: November 7, 2025

    434

    相关实验视频

    Last Updated: Jan 10, 2026

    Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools
    10:41

    Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

    Published on: December 16, 2015

    9.3K
    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

    19.0K
    Analysis of Multidimensional Microscopy Data Using Cell-ACDC
    06:17

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC

    Published on: November 7, 2025

    434

    科学领域:

    • 计算生物学 计算生物学
    • 单细胞RNA测序分析分析
    • 系统生物学 系统生物学

    背景情况:

    • 推断细胞发育轨迹对于理解细胞进展至关重要.
    • 目前用于单细胞RNA测序 (scRNA-seq) 分析的方法仅限于样本内轨迹.
    • 在样本中手动链接细胞是劳动密集型和复杂的,用于交叉样本轨迹的构建.

    研究的目的:

    • 开发一种用于预测跨样本细胞发育轨迹的自动化方法.
    • 为探索和完善这些轨迹创建一个视觉分析系统 (TrajLens).
    • 将空间动力学纳入对多个样本细胞进化的分析.

    主要方法:

    • 提出了一个基于图形神经网络 (GNN) 的模型,用于预测跨样本细胞发育轨迹.
    • 开发了TrajLens,一个具有集成多样本细胞分布和发育方向特征的视觉分析系统.
    • 利用叠加在细胞分布数据上的轮图来进行直观的探索.

    主要成果:

    • 通过使用GNN模型,成功预测了跨样本细胞发育轨迹.
    • TrajLens提供了空间进化模式的概述,并允许直观的探索.
    • 定量评估和案例研究证明了该系统的有效性.

    结论:

    • 基于GNN的模型和TrajLens系统自动化和增强跨样本细胞发育轨迹的分析.
    • 该方法有效地解决了scRNA-seq.中手动交叉样本分析的局限性.
    • 该系统有助于生物学家了解复杂的细胞空间动态和进化路径.