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

Cluster Sampling Method01:20

Cluster Sampling Method

11.9K
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...
11.9K
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
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.4K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.4K
Scatter Plot01:15

Scatter Plot

6.9K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.9K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

2.4K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
2.4K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K

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

A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics.

Bioinformatics (Oxford, England)·2026
Same author

Phagocytic remodeling in CD74<sup>High</sup> tumor-associated macrophages during brain metastasis of lung adenocarcinoma.

Translational cancer research·2026
Same author

FluNexus: A versatile web platform for antigenic prediction and visualization of influenza A viruses.

iMeta·2026
Same author

Mosaic integration of spatial multi-omics with SpaMosaic.

Nature genetics·2026
Same author

Artificial Intelligence Powers Protein Functional Annotation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

scCMA: A Contrastive Masked Autoencoder Framework for Robust Representation Learning of scRNA-seq Data.

Interdisciplinary sciences, computational life sciences·2026

相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

stAA:对抗式图形自编码器用于空间解析转录组学的空间聚类任务.

Zhaoyu Fang1, Teng Liu2,3, Ruiqing Zheng1

  • 1School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.

Briefings in bioinformatics
|January 8, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了stAA,这是空间转录学中空间域识别的新方法. 这种方法提高了聚类和边界检测的准确性,改善了从组织数据中获得的生物见解.

关键词:
具有对抗性的学习.图形自编码器的自编码器图表神经网络的神经网络空间域是一个空间域.空间转录学 空间转录学

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.9K

相关实验视频

Last Updated: Jul 6, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.9K

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:

  • 空间解析的转录学使单细胞基因表达分析具有空间上下文.
  • 空间聚类对于分析空间转录组数据至关重要.
  • 基于图形神经网络 (GNN) 的方法已经提高了空间聚类的准确性.

研究的目的:

  • 为准确的空间域识别提出 stAA,一个对抗性的变量图自编码器.
  • 改进空间域边界的识别,这是该领域的一个持续挑战.

主要方法:

  • stAA利用使用GNN生成细胞嵌入的基因表达和空间信息.
  • 它使用瓦瑟斯坦距离来强制将嵌入式分发强制执行到以前的分发中.
  • 反对训练增强了稳定性和空间域信息捕获,通过预集群标签将全球图形上下文纳入.

主要成果:

  • 在空间聚类准确性方面,stAA的性能优于现有的最先进的方法.
  • 在不同的配置文件平台和分辨率上实现卓越的集群结果.
  • 在生物分析中识别细粒度组织结构,瘤亚型和发育轨迹.

结论:

  • 在空间转录学中,stAA为空间域识别提供了强大而准确的解决方案.
  • 该方法显示了与当前方法相比的显著改进,使更深层次的生物发现成为可能.
  • 它捕捉复杂的空间模式和生物过程的能力突出了其在转录基因数据分析中的潜力.