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

RNA-seq03:21

RNA-seq

11.7K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
11.7K

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

Super-Resolution Compatible DNA Labeling Technique Reveals Chromatin Mobility and Organization Changes During Differentiation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

High-throughput screening of E3 ubiquitin ligases identifies TRIM48 as a novel negative regulator of RIG-I signaling.

Cellular signalling·2025
Same author

Morphology-dependent entry kinetics and spread of influenza A virus.

The EMBO journal·2025
Same author

MethylBERT enables read-level DNA methylation pattern identification and tumour deconvolution using a Transformer-based model.

Nature communications·2025
Same author

DNA choreography: correlating mobility and organization of DNA across different resolutions from loops to chromosomes.

Histochemistry and cell biology·2024
Same author

Replisome loading reduces chromatin motion independent of DNA synthesis.

eLife·2023
Same journal

Delocalized Redox Framework of Indanthrone Enables Low-Strain and Durable Mn<sup>2+</sup>/H<sup>+</sup> Storage in Aqueous Batteries.

Small methods·2026
Same journal

Sandgrouse Feather-Inspired Multiscale Hierarchical Microstructured Surfaces via IICSA for Controlled Liquid Regulation.

Small methods·2026
Same journal

Smart Antibacterial Janus Fabric Based on PVDF/Ag-Decorated-MXene for Unidirectional Water Transport and Thermal Management.

Small methods·2026
Same journal

Synergistic Anion Confinement in a Poly(Ionic Liquid)/MOF Composite Electrolyte Decouples Ionic Conductivity and Mechanical Strength for High-Performance Solid-State Lithium Metal Batteries.

Small methods·2026
Same journal

Fractionation-Free Protein Corona Quantification Through Synchrotron-Based Small-Angle X-ray Scattering.

Small methods·2026
Same journal

Coronamicroparticle Arrays with Stable Superamphiphobicity.

Small methods·2026
查看所有相关文章

相关实验视频

Updated: Jan 10, 2026

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

基于深度学习的质量控制使用亚细胞RNA空间分布模式用于空间转录组学数据中的细胞细分.

Renpeng Ding1, Kerem Celikay1, Ming Ni2

  • 1Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University, 69120, Heidelberg, Germany.

Small methods
|November 28, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种深度学习方法,以改善空间转录组学 (sST) 数据中的细胞细分. 人工智能评估RNA模式以识别和纠正细分错误,提高研究数据质量.

关键词:
细胞细分 细胞细分 细胞细分质量控制质量控制质量控制空间转录学 空间转录学亚细胞RNA的空间分布

更多相关视频

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

624
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

相关实验视频

Last Updated: Jan 10, 2026

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
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

624
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

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:

  • 基于测序的空间转录组学 (sST) 在亚细胞水平上提供高分辨率的转录组分析.
  • 精确的细胞细分在sST数据分析中仍然是一个挑战,阻碍了精确的RNA点分配.
  • 现有的方法缺乏在sST中进行细胞细分的强有力的质量控制.

研究的目的:

  • 开发基于深度学习的质量控制 (QC) 方法,用于sST数据中的细胞细分.
  • 为了提高细胞细分结果的准确性和可靠性.
  • 增强空间转录学数据的整体分析.

主要方法:

  • 一个深度神经网络被设计用于分析亚细胞RNA分布模式.
  • 该方法基于RNA空间特征来识别部分细分和合并的细胞.
  • 质量控制方法与基于变压器的细分模型集成,使用它来改进训练数据集.

主要成果:

  • 深度学习方法有效地评估了sST数据中细分细胞的质量.
  • 部分细分和合并的细胞被准确地识别,解决了常见的细分问题.
  • 将QC方法与基于变压器的细分集成,提高了整体细胞细分性能.

结论:

  • 提出的深度学习方法为质量控制和增强sST中的细胞细分提供了一种新的方法.
  • 这种技术解决了将RNA斑点分配给细胞的关键挑战,改善了数据解释.
  • 该方法显示了利用真实和合成数据集推进空间转录学研究的巨大潜力.