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

10.0K
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...
10.0K

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

Dialysis-related spontaneous anterior mediastinal hemorrhage.

Acta cardiologica·2026
Same author

Ferroptosis, lipid metabolism, and genetic regulation in postoperative rehabilitation of elderly hip fractures: from molecular mechanisms to clinical translation.

Frontiers in genetics·2026
Same author

Synthetic-augmented multimodal deep learning fuses dual-angle RGB images and phenology to unlock genotype-informative canopy structural trait in wheat.

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

<i>Drosophila</i> Myc ameliorates defects in mitochondrial homeostasis and muscle maturation caused by Metaxin-2 deficiency.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Life-threatening haemorrhage from aortic perforation during central venous access.

Acta cardiologica·2026
Same author

Synergistic antenna-modulator integration for a monolithic photonic RF receiver.

Nature communications·2026

相关实验视频

Updated: Jul 15, 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

18.6K

scFseCluster:用于单细胞RNA-seq数据的特征选择增强集群.

Zongqin Wang1, Xiaojun Xie1,2, Shouyang Liu3

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China.

Life science alliance
|October 3, 2023
PubMed
概括

一个新的计算框架scFseCluster通过使用一种新的特征选择方法来改进单细胞RNA测序 (scRNA-seq) 数据分析. 这种方法提高了细胞聚类的准确性和生物研究的效率.

更多相关视频

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
04:21

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

2.9K
Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
05:59

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

727

相关实验视频

Last Updated: Jul 15, 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

18.6K
Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
04:21

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

2.9K
Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans
05:59

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

727

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:

  • 单细胞RNA测序 (scRNA-seq) 揭示了超出大量RNA测序的细胞异质性和功能多样性.
  • 聚类对于scRNA-seq数据分析至关重要,可以识别细胞类型和细胞状态.
  • 现有的计算方法面临着一般化和高计算成本的挑战.

研究的目的:

  • 介绍scFseCluster,这是一个用于scRNA-seq集群的新型计算框架.
  • 通过提高概括性和降低计算成本来解决现有方法的局限性.

主要方法:

  • 开发了scFseCluster,这是一个集成元启发算法 (基于量子松鼠搜索算法的特征选择) 的框架.
  • 该算法提取最佳的基因组,以提高细胞聚类性能.
  • 通过模拟实验和对基准scRNA-seq数据集的比较研究进行验证.

主要成果:

  • scFseCluster 在八个基准scRNA-seq数据集上表现出高性能.
  • 该框架显著超过了七个最先进的集群算法.
  • 在高可变基因上的特征选择被证明可以显著改善聚类结果.

结论:

  • scFseCluster是用于scRNA-seq数据聚类的多功能和有效工具.
  • 集成先进的特征选择可以提高细胞类型识别的准确性和效率.
  • 这一框架为生物研究中单细胞数据分析提供了宝贵的进步.