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

9.9K
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...
9.9K

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

Lycorine Derivative Inhibits SARS-CoV-2 Replication by Reducing -1 Programmed Ribosomal Frameshifting via Targeting ZAP.

MedComm·2026
Same author

MLRR-ATV: A Robust Manifold Nonnegative Low-Rank Representation With Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.

IEEE transactions on computational biology and bioinformatics·2024
Same author

Effects of Pelvic Floor Muscle Massage on the Pregnancy Outcome of Frozen Embryo Transfer in Patients with Thin Endometrium.

Computational and mathematical methods in medicine·2022
Same author

Significance of interstitial fibrosis and p16 in papillary thyroid carcinoma.

Endocrine journal·2022
Same author

Study of Trunk Morphological Imbalance and Rehabilitation Outcome of Adolescent Idiopathic Scoliosis with Intelligent Medicine.

Computational intelligence and neuroscience·2022
Same author

Design, synthesis and in vivo anticancer activity of novel parthenolide and micheliolide derivatives as NF-κB and STAT3 inhibitors.

Bioorganic chemistry·2021

相关实验视频

Updated: Jun 27, 2025

Fluorescence-Activated Nuclei Negative Sorting of Neurons Combined with Single Nuclei RNA Sequencing to Study the Hippocampal Neurogenic Niche
08:16

Fluorescence-Activated Nuclei Negative Sorting of Neurons Combined with Single Nuclei RNA Sequencing to Study the Hippocampal Neurogenic Niche

Published on: October 20, 2022

2.9K

考希超图,拉普拉斯非负矩阵因子化,用于单细胞RNA测序数据分析.

Gao-Fei Wang1, Longying Shen2

  • 1School of Computer Science, Qufu Normal University, Rizhao, 276826, Shandong, China. wanggf66@126.com.

BMC bioinformatics
|April 29, 2024
PubMed
概括

这项研究引入了Cauchy超图拉普拉斯非负矩阵因子化 (CHLNMF) 来通过降低噪声灵敏度来改善单细胞RNA测序 (scRNA-seq) 数据集群. 这种新方法提高了分析复杂生物数据的准确性.

关键词:
考契损失函数函数的作用超图形规范化的规范化非负矩阵因数分解的非负矩阵因数分解样本聚类是指样本的聚类.单细胞RNA测序的一个细胞.

更多相关视频

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

18.5K
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

658

相关实验视频

Last Updated: Jun 27, 2025

Fluorescence-Activated Nuclei Negative Sorting of Neurons Combined with Single Nuclei RNA Sequencing to Study the Hippocampal Neurogenic Niche
08:16

Fluorescence-Activated Nuclei Negative Sorting of Neurons Combined with Single Nuclei RNA Sequencing to Study the Hippocampal Neurogenic Niche

Published on: October 20, 2022

2.9K
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

18.5K
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

658

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) 已经推进了生物学发现.
  • 聚类对于scRNA-seq数据分析至关重要,但对噪声敏感.
  • 现有的方法与生物数据中的高阶空间信息和噪声作斗争.

研究的目的:

  • 为scRNA-seq数据开发一个强大的聚类方法.
  • 解决噪声敏感性,并将高阶关系纳入数据分析.
  • 提高复杂生物数据集中的聚类发现的可靠性.

主要方法:

  • 拟议的考奇超图拉普拉斯非负矩阵因子化 (CHLNMF).
  • 用考契损失函数 (CLF) 取代欧几里德距离,以减少噪声的影响.
  • 集成的超图制约对于高阶样本关系.
  • 为了模型解决方案,利用了半二次优化.

主要成果:

  • 与其他九种方法相比,CHLNMF表现优越.
  • 该方法有效地减少了噪音对集群的影响.
  • 在七个不同的scRNA-seq数据集上得到验证.
  • 实验结果证实了该技术的有效性.

结论:

  • CHLNMF为scRNA-seq数据集群提供了一个强大而准确的方法.
  • 该方法通过考虑高阶相互作用来增强噪音生物数据的分析.
  • 这一进步有助于从单细胞数据中发现生物学见解.