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相关概念视频

RNA-seq03:21

RNA-seq

10.1K
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...
10.1K
Ribosome Profiling02:24

Ribosome Profiling

3.6K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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相关实验视频

Updated: Jul 21, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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评估单细胞RNA-seq数据的归算方法.

Yi Cheng1, Xiuli Ma2, Lang Yuan1

  • 1School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), Peking University, Beijing, 100871, China.

BMC bioinformatics
|July 28, 2023
PubMed
概括
此摘要是机器生成的。

评估单细胞RNA测序 (scRNA-seq) 数据的归算方法至关重要. 不同的方法显示数据集特定的性能,影响细胞聚类和标记基因识别.

关键词:
集群集成是指集群集成.计入计算是指计入计算的方法.一个单细胞的单细胞.这就是scRNA-seqq.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

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相关实验视频

Last Updated: Jul 21, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

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科学领域:

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

背景情况:

  • 单细胞RNA测序 (scRNA-seq) 提供了高通量基因表达数据.
  • 在scRNA-seq中的数据丢失可以掩盖生物信号.
  • 推算方法是为了解决scRNA-seq数据丢失而开发的.

研究的目的:

  • 系统地评估scRNA-seq数据最近的归算算法.
  • 在不同的数据集中比较归算方法的性能.

主要方法:

  • 评估了11种归算方法.
  • 使用了12个真实免疫性scRNA-seq数据集和4个模拟数据集.
  • 根据数值恢复,细胞聚类和标记基因分析来评估性能.

主要成果:

  • 大多数方法都改善了数值回收.
  • 在数据集和协议之间,推算方法的性能各不相同.
  • 没有一个单一的方法在细胞聚类中始终表现出色;一些方法对它产生了负面影响.
  • 一些方法确定了新的细胞子集,但归算挑战仍然存在.

结论:

  • 计量方法的影响是数据集特定的.
  • 该研究强调了各种归算方法的好处和局限性.
  • 为scRNA-seq数据分析提供数据驱动的指导.