Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Weakly supervised forest canopy extraction and multi-dimensional joint canopy entropy for quantifying canopy structural complexity using large-scale forest UAV LiDAR data.

Plant phenomics (Washington, D.C.)·2026

Same author

Efficient control of Tribulium castaneum using zeolitic imidazolate framework-8 nanocarrier-mediated delivery of dsRNA targeting Paramyosin.

Pest management science·2026

Same author

CellLoop: Identifying single-cell 3D genome chromatin loops.

Nature communications·2026

Same author

Isolation, screening, and optimization of the fermentation conditions for endophytic bacteria CHR2-1 of <i>Capparis hainanensis</i>.

Frontiers in microbiology·2026

Same author

HRCHY-CytoCommunity identifies hierarchical tissue organization in cell-type spatial maps.

Nature communications·2026

Same author

pH-driven spontaneous recovery of tyrosine <i>via</i> co-precipitation in the indirect aqueous carbonation of gypsum.

RSC advances·2026

Search research articles

相关实验视频

Updated: Jan 10, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

从空间转录组学数据中推断细胞类型组成和单细胞空间地图,使用SWOT.

Lanying Wang¹, Yuxuan Hu¹, Lin Gao²

¹School of Computer Science and Technology, Xidian University, Xi'an, China.

Communications biology

|November 19, 2025

概括

SWOT是一种新的方法,将细胞映射到斑点,使单细胞空间地图能够从空间转录学数据中绘制出来. 这个工具以单细胞分辨率重建细胞社区和组织架构.

科学领域:

单细胞转录组学单细胞转录组学
空间转录组学空间转录组学
计算生物学是一种计算生物学.

背景情况:

空间转录学数据往往缺乏单细胞分辨率.
现有的细胞类型解方法可以估计比例,但不能估计单个细胞的位置.
重建单细胞空间地图对于理解组织结构至关重要.

研究的目的:

开发一种方法,从基于点的空间转录组学数据推断单细胞空间地图.
为了克服当前空间转录学分析的分辨率限制.
为了使组织内的细胞水平的发现.

主要方法:

引入了一个空间加权最佳运输 (SWOT) 方法.
开发了一种细胞到点映射方法.
应用SWOT来推断细胞类型组成和单细胞空间地图.

主要成果:

SWOT准确地估计了细胞类型比例和每个点的细胞数量.
该方法精确地确定单个细胞的空间坐标.
SWOT可视化细胞类型的空间分布,并绘制组织中单细胞的地图.
SWOT 帮助识别和注释细胞社区.

更多相关视频

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

相关实验视频

Last Updated: Jan 10, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

结论:

SWOT将点分辨率空间转录组学数据转换为单细胞分辨率.
这种方法促进了组织结构和细胞组织的详细分析.
在空间生物学中,SWOT是推动细胞水平发现的宝贵工具.