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

9.7K
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...
9.7K

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

Servo-Actuated 3D-Printed Disposable Microvalves for Automated, Scalable Organoid Culture in Standard Incubators.

bioRxiv : the preprint server for biology·2026
Same author

Cloud-connected pluripotent stem cell platform enhances scientific identity in underrepresented students.

Stem cell reports·2026
Same author

Spatially defined axonal guidance in neural organoids with micropatterned microfluidic channels.

bioRxiv : the preprint server for biology·2026
Same author

SpikeLab: Agentic tools for spike data analysis.

bioRxiv : the preprint server for biology·2026
Same author

Towards adaptive bioelectronic wound therapy with integrated real-time diagnostics and machine learning-driven closed-loop control.

npj biomedical innovations·2026
Same author

A multi therapy bioelectronic wound dressing.

npj biomedical innovations·2026
Same journal

Protocol for ultrasound-guided injection into the murine portal vein to initiate liver metastasis.

STAR protocols·2026
Same journal

Protocol for semi-automatic quantitative bioimaging analysis of synapse loss.

STAR protocols·2026
Same journal

Protocol for integrated ubiquitination analysis of in vitro E3 ligase-DUB regulation and in vivo ubiquitin chain linkage characterization.

STAR protocols·2026
Same journal

Protocol for constructing multi-ancestry polygenic models using S4-Multi.

STAR protocols·2026
Same journal

Protocol for inducing deep vein thrombosis in C57BL/6J mice using the inferior vena cava stenosis model.

STAR protocols·2026
Same journal

Overcoming the challenges of genome-editing essential genes.

STAR protocols·2026
查看所有相关文章

相关实验视频

Updated: Apr 13, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

14.5K

在单细胞RNA测序数据中,深度学习驱动的细胞类型标签转移协议.

Zoe Zabetian1, Jesus Gonzalez-Ferrer1, Julian Lehrer2

  • 1Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Live Cell Biotechnology Discovery Lab, University of California, Santa Cruz, Santa Cruz, CA 95060, USA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA 95060, USA.

STAR protocols
|April 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种可扩展,可解释的单细胞机器学习 (SIMS) 协议,用于将单细胞RNA测序数据中的细胞类型标签转移. 该方法可以在数据集中准确地识别和分析细胞.

关键词:
发育生物学是发展生物学.在RNA-seqqq.序列化是指测序的使用.

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

19.2K
Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
08:30

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

14.1K

相关实验视频

Last Updated: Apr 13, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

14.5K
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

19.2K
Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
08:30

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

14.1K

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 生成的高维数据对于理解细胞异质性至关重要.
  • 准确的细胞类型注释是scRNA-seq数据分析的一个基本挑战.
  • 转移细胞类型标签可以加速分析,并使跨数据集的比较.

研究的目的:

  • 为在scRNA-seq数据中使用SIMS进行细胞类型标签转移提供一个标准化的协议.
  • 为数据准备,模型训练和预测解释提供一个用户友好的框架.
  • 为了促进用于scRNA-seq分析的先进机器学习工具的可访问性.

主要方法:

  • 开发SIMS的协议,SIMS是一个用于单细胞数据的机器学习框架.
  • 数据准备的步骤说明,包括处理和预处理.
  • 使用标记数据集的模型训练或使用预先训练的模型推断用于细胞类型注释.

主要成果:

  • 一个全面的协议,详细说明SIMS用于细胞类型标签转移的应用.
  • 可视化,下载和解释预测的细胞类型标签的方法.
  • 通过API,GitHub代码空间和Web应用程序展示SIMS的可访问性.

结论:

  • 该SIMS协议为scRNA-seq数据中的细胞类型注释提供了一个强大的和可访问的方法.
  • 这种方法提高了机器学习在单细胞基因组学中的可解释性和可扩展性.
  • 提供的资源使研究人员能够有效地转移细胞类型标签,并推进生物见解.