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相关概念视频

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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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...
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相关实验视频

Updated: Jul 24, 2025

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

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概率张量分解从单细胞多原子数据中提取出更好的潜伏嵌入.

Ruo Han Wang1, Jianping Wang1, Shuai Cheng Li1

  • 1Department of Computer Science, City University of Hong Kong, Hong Kong.

Nucleic acids research
|July 5, 2023
PubMed
概括

新的框架SCOIT使用张量分解分析多原子单细胞数据. 它改善了细胞聚类和基因表达分析,优于剖析细胞异质性的现有方法.

科学领域:

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

背景情况:

  • 单细胞测序产生了多原子数据,通常被表示为更高等级的张量.
  • 现有的分析工具经常将张量数据减少到矩阵,失去特征对应.
  • 这限制了复杂的单细胞多原子数据集的全面分析.

研究的目的:

  • 介绍SCOIT,用于单细胞多原子数据分析的概率张量分解框架.
  • 从复杂的数据集中提取有意义的细胞,基因和欧米嵌入.
  • 加强下游分析,如细胞聚类,基因表达研究和数据归算.

主要方法:

  • 开发了一个名为SCOIT的概率张量分解框架.
  • 整合了高斯式,波松式和负二项式分布来处理数据稀疏性,噪声和异质性.
  • 将框架应用于八个不同的单细胞多原子数据集.

主要成果:

  • 与九种最先进的工具相比,SCOIT实现了优越的细胞聚类性能.
  • 基因嵌入使得有效的跨欧米基因表达分析和监管网络研究成为可能.
  • 交叉omics的归算性能显著改善 (3.38-39.26%的PCC增加),处理缺失的omic配置文件.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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结论:

  • 使用多原子单细胞数据,SCOIT有效地剖析细胞异质性.
  • 该框架提供了强大的工具,用于整合性奥米克分析和归算.
  • SCOIT通过适应部分可用的omic配置文件的数据集来表现出灵活性.