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

10.4K
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...
10.4K

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

Integrated transcriptomic analysis reveals lymphatic <i>Icam1</i>-mediated immune dynamics after myocardial infarction.

Zoological research·2026
Same author

A mechanobiological hypothesis on bone cement-induced progression of bone metastases.

Frontiers in bioengineering and biotechnology·2026
Same author

A Dual-Focus Workflow for Simultaneously Engineering High Thermostability of Aldo-Keto Reductase for the Degradation of 3-Keto-Deoxynivalenol.

Journal of agricultural and food chemistry·2026
Same author

Structural basis of NMI-IFP35 domains and swapping phenomenon in IFP35-NID.

Journal of structural biology: X·2026
Same author

Integrated Single-Cell and Spatial Analysis Reveals a Metabolic-Immune Axis Driving Aortic Dissection.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Latent transition analysis of stigma and its association with treatment adherence in pulmonary tuberculosis patients during anti-tuberculosis therapy.

Frontiers in public health·2026
Same journal

Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.

Interdisciplinary sciences, computational life sciences·2026
Same journal

DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.

Interdisciplinary sciences, computational life sciences·2026
Same journal

STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.

Interdisciplinary sciences, computational life sciences·2026
Same journal

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

Interdisciplinary sciences, computational life sciences·2026
Same journal

Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.

Interdisciplinary sciences, computational life sciences·2026
Same journal

ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.

Interdisciplinary sciences, computational life sciences·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

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

18.7K

用低级矩阵因子化和局部图规则化集群单细胞RNA-Seq数据

Yue Yu1, Wei Zhang2,3, Xiaoying Zheng4

  • 1School of Sciences, East China Jiaotong University, Nanchang, 330013, China.

Interdisciplinary sciences, computational life sciences
|September 2, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新型聚类算法,即单细胞RNA测序 (scRNA-seq) 数据. 通过强大的处理噪音,高维度数据以获得更好的生物洞察力,LRMGC准确地识别细胞类型.

关键词:
集群化局部图表正规化低级别的代表施特的规范单细胞RNA测序

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

488

相关实验视频

Last Updated: Sep 9, 2025

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

18.7K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
08:58

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

488

科学领域:

  • 基因组学
  • 生物信息学
  • 计算生物学

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 可以研究细胞异质性.
  • 准确的细胞类型识别对于scRNA-seq数据分析至关重要.
  • 现有的低级别表示 (LRR) 集群方法与scRNA-seq数据的高维度,稀疏性和噪音相斗争.

研究的目的:

  • 从scRNA-seq数据开发一个新的聚类算法,以准确和强大的细胞类型识别.
  • 解决基于LRR的现有方法在噪音和异常值中捕获真正的生物模式的局限性.
  • 改进scRNA-seq研究中的下游分析.

主要方法:

  • 引入了一种新的集群算法:用局部图形规范化 (LRMGC) 进行低等级矩阵分解.
  • 用于表示矩阵的三分解策略,并将Schatten p-norm应用于核心矩阵,以实现强大的相似性学习.
  • 集成的局部分流规范化和角度对齐,以提高集群性能.

主要成果:

  • 与scRNA-seq数据集上的先进方法相比,LRMGC的性能和可靠性都更高.
  • 算法有效地发现了细胞类型组成,即使存在噪音和异常值.
  • 下游分析,包括标记基因识别和罕见细胞识别,证实了LRMGC的有效性.

结论:

  • 在scRNA-seq数据中,LRMGC提供了准确且可靠的细胞类型识别方法.
  • 这种方法有效地保护了底层子空间结构,同时减轻了噪音.
  • 使用scRNA-seq数据的LRMGC提高了下游生物研究的可靠性.