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Ribosome Profiling

Ribosome Profiling

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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Updated: Sep 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

基于模型的尺寸缩小用于单细胞RNA-seq使用一般化的双线模型.

Phillip B Nicol¹, Jeffrey W Miller¹

¹Department of Biostatistics, Harvard University, 677 Huntington Ave, Boston, MA, 02115, United States.

Biostatistics (Oxford, England)

|August 12, 2025

概括

此摘要是机器生成的。

scGBM是一种用于单细胞RNA-seq (scRNA-seq) 数据分析的新方法. 它通过直接建模计数,捕捉生物变异和量化不确定性来改善细胞聚类,从而提供了改进的维度减小.

关键词:

缩小尺寸缩小尺寸的方法一般化的线性模型.一个单细胞RNA测序的序列.

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Last Updated: Sep 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

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:

减小尺寸对于单细胞RNA-seq (scRNA-seq) 数据分析至关重要.
像PCA这样的标准方法可以引入文物并掩盖真正的生物信号.
现有的基于计数的模型通常是计算密集的,缺乏不确定性量化.

研究的目的:

为scRNA-seq数据开发一种新的,可扩展和基于模型的维度减少方法.
解决处理大型数据集和量化不确定性的现有方法的局限性.
为了提高低维嵌入的生物解释性.

主要方法:

开发了scGBM,一种利用Poisson双线模型进行scRNA-seq维度缩小的方法.
实现了一个基于代重量单数值分解的快速估计算法.
包含了细胞潜伏位置的不确定性量化和集群信心评估.

主要成果:

scGBM有效地扩展到数百万个细胞的数据集.
该方法产生了低维嵌入,更好地捕获生物信息.
scGBM成功地从scRNA-seq数据中删除了不需要的变异.
不确定性量化有助于评估细胞聚类的信心.

结论:

scGBM提供了一个计算效率高,并且在统计学上强大的方法来减少scRNA-seq的维度.
该方法增强了真正的生物变异性的识别,并减少了虚假的异质性.
scGBM为分析大规模scRNA-seq数据集和改进细胞聚类等下游分析提供了有价值的工具.