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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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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...
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Protein Networks02:26

Protein Networks

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RNA-seq03:21

RNA-seq

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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...
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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Time-Series Graph00:54

Time-Series Graph

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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...
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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相关实验视频

Updated: Jun 10, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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DGSIST:基于深度图形结构的空间转录组数据集群 Infomax

Yu-Han Xiu1, Si-Lin Sun1, Bing-Wei Zhou1

  • 1College of Information Science Technology, Hainan Normal University, HaiKou City 571158, China; Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, HaiKou City 571158, China.

Methods (San Diego, Calif.)
|October 16, 2024
PubMed
概括
此摘要是机器生成的。

深度图形结构Infomax (DGSI) 模型和DGSIST框架利用空间转录学数据进行准确的细胞聚类和空间域识别. 这种无监督的方法增强了对组织组织和疾病结构的理解.

关键词:
集群集成是指集群集成.这是一个DGSIST.图表卷积神经网络的图.图形神经网络是一个神经网络.空间转录组学 空间转录组学

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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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:

  • 空间转录学提供了对组织基因表达和结构的洞察力,但往往不充分利用空间数据.
  • 图形神经网络提供了一个将空间信息与基因表达数据集成的机会.
  • 现有的方法可能无法充分利用可用于转录数据集的丰富空间环境.

研究的目的:

  • 开发一个无监督模型,DGSI (深度图形结构Infomax),用于从空间转录组学处理图形数据.
  • 引入DGSIST框架,将DGSI与尺寸缩小和聚类集成在一起,以准确识别细胞类型.
  • 加强空间转录学数据的分析,改进细胞聚类和空间域识别.

主要方法:

  • 使用图形卷积神经网络和无监督学习方法开发了DGSI模型,以最大限度地提高图形和节点表示之间的相互信息.
  • 集成的DGSI与Singular Value Decomposition (SVD) 和k-means++用于DGSIST无监督细胞聚类框架.
  • 将DGSIST应用于不同组织类型和技术的各种空间转录组数据集.

主要成果:

  • DGSIST准确地识别了细胞类型和空间域,性能优于现有的方法.
  • 该框架有效地消除了批量效应,没有明确的纠正.
  • 在各种组织类型和技术平台上表现出强大的性能.

结论:

  • DGSIST是一种强大的无监督框架,用于细胞聚类和使用空间转录学数据进行空间分析.
  • 该模型有效地捕获本地空间信息,从而提高了识别细胞结构的准确性.
  • 在癌症等疾病中,DGSIST具有显著的潜力,可以促进对空间组织的理解.