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RNA-seq

RNA-seq

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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Updated: Jul 1, 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

分析单细胞RNA测序与拓非负矩阵因子化.

Yuta Hozumi¹, Guo-Wei Wei^1,2,3

¹Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.

Journal of computational and applied mathematics

|March 11, 2024

概括

此摘要是机器生成的。

拓非负矩阵因子化 (TNMF) 和强大的TNMF (rTNMF) 增强单细胞RNA测序 (scRNA-seq) 数据分析. 这些新的方法为复杂的生物数据提供了卓越的性能和多尺度分析.

关键词:

代数拓学是一种代数拓学.持续的拉普拉西亚语减少维度,减少维度.机器学习是机器学习.这就是scRNA-seqq.

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Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

相关实验视频

Last Updated: Jul 1, 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

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Author Spotlight: Deciphering the Cellular Mysteries of Intermuscular Adipose Tissue in Humans

Published on: May 3, 2024

科学领域:

计算生物学计算生物学
数据科学数据科学数据科学
统计统计统计统计

背景情况:

单细胞RNA测序 (scRNA-seq) 产生高维,复杂的数据,引起了计算生物学和数据科学领域的重大兴趣.
非负矩阵因子化 (NMF) 是一种减小维度的技术,提供元基因解释,但缺乏多尺度分析能力.

研究的目的:

引入新的非负矩阵因子化 (NMF) 方法,用于增强单细胞RNA测序 (scRNA-seq) 数据分析.
解决现有的NMF方法的局限性,特别是缺乏多尺度分析.

主要方法:

开发了两个持久的拉普拉斯规范化NMF方法:拓NMF (TNMF) 和强大的拓NMF (rTNMF).
在12个不同的scRNA-seq数据集上评估了TNMF和rTNMF.
使用TNMF和rTNMF进行统一多重近似和投影 (UMAP) 和t分布式随机邻近嵌入 (t-SNE) 的可视化.

主要成果:

在所有测试的数据集中,TNMF和rTNMF显著优于现有的基于NMF的方法.
提出的方法为scRNA-seq数据提供了有效的多尺度分析.
TNMF和rTNMF在增强UMAP和t-SNE等数据可视化技术方面表现出实用性.

结论:

在scRNA-seq数据分析中,TNMF和rTNMF代表了NMF的重大进步.
这些方法为探索复杂的生物数据提供了更好的性能和新的能力.
拓规范化提高了scRNA-seq.的NMF组件的可解释性和稳定性.