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

ESRP1-regulated DNMT3B isoform switching determines the malignant potential of pancreatic cancer.

Oncogene·2026

Same author

Mapping the Cell-Cell Interactions in Cardiovascular Diseases: A Single-Cell Atlas Perspective.

Korean circulation journal·2026

Same author

<i>CEBPB</i> Expression in Tumor Cells Drives Immune Evasion in Colorectal Cancer via <i>CTLA4</i> Up-regulation in T Cells.

Cancer communications (London, England)·2026

Same author

CluVar: clustering of variants using autoencoder for inferring cancer subclones from single cell RNA sequencing data.

Briefings in bioinformatics·2025

Same author

FastTENET: an accelerated TENET algorithm based on manycore computing in Python.

Bioinformatics (Oxford, England)·2024

Same author

Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases.

Briefings in functional genomics·2024

Search research articles

相关实验视频

Updated: Jan 12, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

从像素到细胞类型:对空间转录学,解卷学计算方法的全面审查.

Jahanzeb Saqib¹, Junil Kim^2,3

¹Department of Bioinformatics, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.

Genomics & informatics

|November 1, 2025

概括

空间转录学揭示了组织内的基因表达. 本综述分析了20个解卷算法,这些算法对于从有限分辨率的空间转录学数据中理解细胞组成至关重要.

关键词:

贝叶斯的推理贝叶斯的推理细胞类型的解细胞类型.解卷算法解卷算法解卷算法解卷算法深度学习是一种深度学习.基于图形的建模.非负矩阵因子分解 (NMF)可能性的建模.一个单细胞RNA-seqq.空间转录组学空间转录组学基于变压器的模型

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

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

相关实验视频

Last Updated: Jan 12, 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

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

空间转录学技术通过保存空间基因表达数据,提供了对组织结构和细胞相互作用的洞察.
当前平台的分辨率有限导致捕获点的混合信号,需要计算解卷来确定细胞组成.

研究的目的:

为二十个空间转录学解卷算法提供全面的审查和分析.
阐明这些算法的方法论基础,计算原理和数据处理范式.
作为研究人员理解,选择和应用解卷工具的手册.

主要方法:

对20个空间转录学解卷算法的比较分析.
专注于基础计算算法,建模方法和数据处理管道.
评估算法如何处理外部引用,噪音和数据稀疏性的评估.

主要成果:

详细比较各种解卷方法的概念和技术基础.
在算法设计和数据处理中识别不同的方法.
了解不同解体策略的优缺点.

结论:

对空间转录学解卷算法的彻底理解对于推进生物研究至关重要.
这次审查为研究人员提供了导航计算环境和选择适当工具的知识.
促进开发新型解卷策略,并明智地应用现有方法.