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

Variability: Analysis01:11

Variability: Analysis

415
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
415
Thematic Layering in GIS01:30

Thematic Layering in GIS

296
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
296
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

243
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
243

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

Crosstalk In: crosstalk-aware inference of tumor microenvironment cell infiltration.

Journal of translational medicine·2026
Same author

HiCAF-Net: A Hierarchical Cross-Attention Fusion framework for cross-cancer subtype classification using histopathological and genomic data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Urban river health evaluation in semi-arid Xi'an, China: a hybrid RF-DNN framework integrating multi-source and SHAP-based interpretability.

Environmental monitoring and assessment·2026
Same author

Treatment Preferences in Adult Patients with Type 2 and Type 3 Spinal Muscular Atrophy: Evidence from a Discrete Choice Experiment in China.

The patient·2026
Same author

The Role of Non-stigmatizing Attitudes Toward Peers With Mental Health Problems in Shaping Adolescents' Attitudes Toward Professional Psychological Help-Seeking.

The Psychiatric quarterly·2026
Same author

Multimedia migration and ecological risks of bisphenol A in semiarid urban rivers: a case study of Xi'an, China.

Environmental monitoring and assessment·2026
Same journal

The lncRNA-m6A axis in cancer: a bidirectional regulatory network in tumor progression and therapeutic resistance.

Journal of translational medicine·2026
Same journal

Repurposing cepharanthine as a radiosensitizer in esophageal squamous cell carcinoma through dual metabolic intervention and direct targeting of p70s6K.

Journal of translational medicine·2026
Same journal

Cellular crosstalk and signaling networks in the rheumatoid arthritis synovial microenvironment.

Journal of translational medicine·2026
Same journal

Pilot spatial transcriptomics of dental pulpitis suggests immune-fibroblast profiling linked to reversibility.

Journal of translational medicine·2026
Same journal

Beyond semen analysis: in men with normal semen parameters telomere attrition and oxidative imbalance distinguish those fertile from those with infertility.

Journal of translational medicine·2026
Same journal

Dual-block HER2 assessment reveals clinically relevant intratumoral heterogeneity in gynecologic cancers: a single-center landscape analysis.

Journal of translational medicine·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

613

HiSTaR:用层次空间转录学变化自编码器识别空间域.

Junhua Yu1, Jiaqi Yuan1, Qianbei Yi1

  • 1Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 511370, China.

Journal of translational medicine
|December 24, 2025
PubMed
概括
此摘要是机器生成的。

新型深度学习工具HiSTaR通过识别组织域和纠正批量效应来增强空间转录组学分析. 这种方法提高了对组织微环境和基因表达模式的理解.

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

5.3K

相关实验视频

Last Updated: Jan 8, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

613
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.1K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

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

Published on: July 6, 2022

5.3K

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:

  • 空间转录组学 (ST) 能够通过空间上下文实现转录组范围的数据采集.
  • 了解组织微环境和空间领域在生物研究中至关重要.
  • 深度学习方法对于分析复杂的ST数据是有效的.

研究的目的:

  • 为了介绍HiSTaR,一个对ST数据的等级变量自编码器.
  • 为了利用多层次的潜伏特征进行增强的空间转录学分析.
  • 改进ST数据中的空间域识别和批量效应校正.

主要方法:

  • 开发了层次空间转录学变化自编码器 (HiSTaR).
  • 采用多个HiSTaR块来捕获空间点的多层次隐藏特征.
  • 利用潜伏特征进行下游分析,如空间域识别和批次校正.

主要成果:

  • 在各种ST数据集的空间域识别中,HiSTaR表现出卓越的性能.
  • 该方法成功地整合了多个组织切片,在没有外部工具的情况下纠正批量效应.
  • HiSTaR支持轨迹和差异基因表达分析,验证其有效性.

结论:

  • HiSTaR为空间转录学研究提供了一个有效的计算框架.
  • 层次特征捕获可以改善空间域的识别和对组织异质性的理解.
  • HiSTaR有可能推进对空间解析基因表达模式的研究.