Jove
Visualize
联系我们

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

Improving the filtering of false positive single nucleotide variations by combining genomic features with quality metrics.

Bioinformatics (Oxford, England)·2023
Same author

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways.

Integrative biology : quantitative biosciences from nano to macro·2022
Same author

MODOMICS: a database of RNA modification pathways. 2021 update.

Nucleic acids research·2021
Same author

Integration of transcriptomic profile of SARS-CoV-2 infected normal human bronchial epithelial cells with metabolic and protein-protein interaction networks.

Turkish journal of biology = Turk biyoloji dergisi·2020
Same author

FLASH Irradiation Spares Lung Progenitor Cells and Limits the Incidence of Radio-induced Senescence.

Clinical cancer research : an official journal of the American Association for Cancer Research·2019
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

相关实验视频

Updated: Jun 27, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

651

SUMA:一个轻量级的机器学习模型为 scRNA-Seq 数据提供了基于最近邻近的共享聚类应用程序接口.

Hamza Umut Karakurt1,2, Pınar Pir1,2

  • 1Department of Bioengineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkiye.

Turkish journal of biology = Turk biyoloji dergisi
|April 29, 2024
PubMed
概括

SUMA是一个新的工具,它使用随机森林模型来优化基于图形的集群,用于单细胞RNA测序 (scRNA-Seq) 数据. 它准确地预测了最佳邻居数量,改善了细胞类型的注释,并为研究人员简化了分析.

关键词:
RShiny 闪亮的 闪亮的这就是ScRNA-Seqq.集群集成是指集群集成.机器学习是机器学习.随机的森林随机的森林

更多相关视频

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
10:44

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

4.1K
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

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
07:35

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

651
Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
10:44

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

4.1K
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.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 单细胞RNA测序 (scRNA-Seq) 揭示了细胞多样性,但面临着数据稀疏性,噪声和细胞类型识别方面的挑战.
  • 基于图形的集群是一种强大的scRNA-Seq分析方法,但其性能严重依赖于用户定义的参数,如邻居数量.
  • 优化这些参数对于scRNA-Seq研究中精确的细胞聚类和注释至关重要.

研究的目的:

  • 开发SUMA,一种使用随机森林模型来预测基于图形的scRNA-Seq数据集群的最佳参数的轻量级工具.
  • 通过优化聚类结果来提高细胞类型注释的准确性和方便性.
  • 将SUMA集成到RShiny应用程序中,以便研究人员,包括非生物信息学家,可以轻松访问.

主要方法:

  • 利用公开可用的scRNA-Seq数据集和三种基于图形的集群算法来开发SUMA.
  • 使用Scikit-learn (Python) 和randomForest (R) 库训练了一个随机森林模型,考虑了广泛的邻居计数和变异基因.
  • 使用调整后的兰德指数 (ARI) 与真实标签对比,评估集群质量,将数据分为培训和测试集.

主要成果:

  • 开发的机器学习模型实现了0.96的精度和0.98.98的AUC.
  • 该模型将scRNA-Seq数据中的细胞数确定为确定最佳邻居数量的最有影响力的特征.
  • SUMA有效地预测了最佳集群参数,从而改善了scRNA-Seq数据分析.

结论:

  • SUMA提供了一种准确和自动化的方法来优化scRNA-Seq数据的基于图形的聚类.
  • SUMAShiny应用程序提供了一个集成的平台,用于集群和可视化scRNA-Seq数据,可通过桌面或Web浏览器访问.
  • SUMA使研究人员,包括那些没有广泛的生物信息学专业知识的研究人员,能够进行强大的细胞类型注释和分析.