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相关概念视频

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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相关实验视频

Updated: Jul 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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整合单细胞RNA-seq数据集与大量批量效应.

Karin Hrovatin1,2,3,4, Amir Ali Moinfar1,5, Luke Zappia1,5

  • 1Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.

bioRxiv : the preprint server for biology
|November 14, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了一种用于整合单细胞RNA测序 (scRNA-seq) 数据集的新方法,改进批量效应去除,同时保留复杂系统的生物变异. 这种方法在scRNA-seq分析中增强了细胞状态和条件解释.

关键词:
KL正规化强度是KL的正规化强度.吸血鬼 之前 之前具有对抗性的学习.基准测试 (benchmarking) 是一种比较的方法.数据整合数据集成.隐性周期一致性 隐性周期一致性单细胞RNA测序 (scRNA-seq) 是一种

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Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing
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Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing
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科学领域:

  • 计算生物学是一种计算生物学.
  • 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) 数据集成对于生物学见解至关重要.
  • 条件变异自编码器 (cVAEs) 对于scRNA-seq集成非常受欢迎.
  • 现有的方法难以协调各种数据集 (例如,跨物种,跨协议).

研究的目的:

  • 开发和比较基于cVAE的scRNA-seq数据集成的新规化策略.
  • 解决当前处理大量技术和生物变异的方法的局限性.
  • 为复杂的生物系统提出一个最佳的cVAE战略.

主要方法:

  • 对cVAE进行替代规范化技术的实施和评估.
  • 对数据集成的VampPrior和高斯式prior进行比较.
  • 对抗性学习的循环一致性损失的评估 (GLUE模型).
  • 评估Kullback-Leibler (KL) 分歧规范化强度调整.

主要成果:

  • 与高斯先验相比,VampPrior显著改善了生物变异保护和批次校正.
  • 循环一致性损失在保存生物信息方面优于对抗性学习.
  • 仅仅调整KL调节强度是不推的,因为它会随意删除生物和批量信息.
  • 一个结合VampPrior和循环一致性损失的新型模型显示出卓越的性能.

结论:

  • 提出了一个新的基于cVAE的集成策略,将VampPrior和循环一致性损失结合起来.
  • 这种最佳策略增强了复杂scRNA-seq数据集中细胞状态和生物条件的下游解释.
  • 拟议的模型,sysVI,可以在scvi-tools包中获得,以实现更广泛的可访问性.
  • 规范化技术为其他基于cVAE的模型提供了潜在的改进.