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
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
140
Cluster Sampling Method01:20

Cluster Sampling Method

12.7K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.7K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
Residual Plots01:07

Residual Plots

5.0K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
5.0K

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

Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers.

BMC medical imaging·2026
Same author

Study on the canopy structure and light distribution of <i>Hippophae rhamnoides</i> at different ages.

Frontiers in plant science·2026
Same author

A Diffusion-Based Time-Frequency Dual-Stream Contrastive Learning Model for Multivariate Time Series Anomaly Detection.

Entropy (Basel, Switzerland)·2026
Same author

Time-resolved transcriptomic analysis reveals key regulatory genes and auxin-responsive networks underlying axillary bud branching in <i>Hippophae rhamnoides</i>.

Frontiers in plant science·2026
Same author

Radioactivity levels of radiocesium and <sup>90</sup>Sr in the surface water of the low-latitude western-central North Pacific Ocean in 2017.

Marine pollution bulletin·2026
Same author

scDGCL: A Dual-Level and Graph-Constrained Contrastive Learning Method for Single-Cell RNA Sequencing Data Clustering.

IEEE transactions on computational biology and bioinformatics·2026

相关实验视频

Updated: Sep 11, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.1K

sigRGCN:一个强大的残余图形卷积网络用于scRNA-Seq数据集群.

Zhenqiu Shu, Min Xia, Kaiwen Tan

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括

    这项研究介绍了sigRGCN,这是一种用于单细胞RNA测序 (scRNA-seq) 数据分析的新型图形卷积网络. sigRGCN通过提高对噪声的强度和防止信息丢失来增强细胞聚类.

    科学领域:

    • 计算生物学 计算生物学
    • 生物信息学是一种生物信息学.
    • 基因组学就是基因组学.

    背景情况:

    • 聚类对于单细胞RNA测序 (scRNA-seq) 分析至关重要,使细胞类型的发现成为可能.
    • 图形卷积网络 (GCN) 对于scRNA-seq集群是有效的,但会受到噪声敏感性和过度平滑的影响.
    • 这些局限性阻碍了准确捕获细胞特异信息和更高阶关系.

    研究的目的:

    • 为改进scRNA-seq数据集群开发一个强大的图形卷积网络模型.
    • 为了应对现有的scRNA-seq分析GCN方法固有的噪声敏感性和过度平滑的挑战.
    • 通过先进的聚类技术,提高细胞类型识别的准确性和可靠性.

    主要方法:

    • 建议sigRGCN,一个包含图形结构优化的残余图形卷积网络.
    • 构建了一个带有注入噪声的扰动细胞图,并使用GCN来减轻噪声影响.
    • 利用L层残留的GCN来对抗过度平滑和捕获更高阶细胞关系.
    • 使用自主监督学习方法优化模型.

    主要成果:

    • 拟议的sigRGCN模型在聚类现实世界scRNA-seq数据方面表现出显著的稳定性.
    • 有效地缓解了过度光滑的问题,从而改善了细胞表征.

    更多相关视频

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    870
    Identification of Circular RNAs using RNA Sequencing
    08:25

    Identification of Circular RNAs using RNA Sequencing

    Published on: November 14, 2019

    12.4K

    相关实验视频

    Last Updated: Sep 11, 2025

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.1K
    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
    04:17

    DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

    Published on: May 10, 2024

    870
    Identification of Circular RNAs using RNA Sequencing
    08:25

    Identification of Circular RNAs using RNA Sequencing

    Published on: November 14, 2019

    12.4K
  • 在九个不同的scRNA-seq数据集中实现了竞争性表现.
  • 成功捕获了高阶细胞关系,提高了聚类的准确性.
  • 结论:

    • sigRGCN为scRNA-seq数据集群提供了强大而有效的解决方案.
    • 该模型成功地克服了传统GCN在这个领域的关键局限性.
    • 结果表明sigRGCN有潜力推进从scRNA-seq数据中发现细胞类型和生物见解.