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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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Classification of Epithelial Tissues: Simple Epithelium01:30

Classification of Epithelial Tissues: Simple Epithelium

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Simple epithelium consists of a single layer of cells that lines body cavities and blood vessels. The shape of the cells in the epithelium reflects the function of the tissue. Cells in simple squamous epithelium appear as thin scales with flat, elliptical nuclei that mirror the form of the cell.
Because of the thinness of the cells, simple squamous epithelium is present where the rapid passage of chemical compounds is observed. For example, the endothelium that lines the capillaries and vessels...
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相关实验视频

LIDER:基于细胞嵌入的深度神经网络分类器,用于监督的细胞类型识别.

Yachen Tang1, Xuefeng Li1, Mingguang Shi1

  • 1Hefei University of Technology, Hefei, China.

PeerJ
|August 21, 2023
PubMed
概括
此摘要是机器生成的。

深度监督学习方法LIDER使用细胞嵌入和神经网络准确地识别单细胞RNA测序数据中的细胞类型. 这种方法提供了强大的性能,与现有方法相比或优于现有方法.

关键词:
细胞嵌入 细胞嵌入细胞类型识别 细胞类型识别深度神经网络分类器堆叠的无噪声自动编码器

相关实验视频

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RNA测序 (scRNA-seq) 生成大型数据集,需要有效的细胞类型识别.
  • 目前的方法通常依赖于无监督的集群,然后是手动注释,这可能是耗时和主观的.

研究的目的:

  • 开发一种深度监督学习方法,用于在scRNA-seq数据中自动识别细胞类型.
  • 引入LIDER (基于细胞嵌入的深度神经网络分类器) 作为这项任务的新方法.

主要方法:

  • LIDER使用一个堆叠的否定自动编码器来学习细胞嵌入.
  • 一个深度神经网络分类器被用来预测基于这些嵌入的细胞类型.
  • 一个定制的重建损失函数被整合到自动编码器中.

主要成果:

  • 在八个不同的scRNA-seq数据集中,LIDER证明了精确的细胞类型识别.
  • 性能与最先进的方法可比或优于.
  • 该方法显示对批量效应的稳定性,这是scRNA-seq分析中的一个常见挑战.

结论:

  • 在LIDER中实施的深度监督学习显示了在scRNA-seq数据中自动化细胞类型识别的巨大潜力.
  • 利德为传统方法提供了准确,坚固和高效的替代方案.
  • LIDER代码的可用性有助于其采用和进一步开发.