Jove
Visualize
联系我们

相关概念视频

RNA-seq03:21

RNA-seq

10.0K
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...
10.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jul 5, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
08:30

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

13.0K

对单细胞RNA-seq数据的归算使用非负矩阵因子化和转移学习.

Jiadi Zhu1, Youlong Yang1

  • 1School of Mathematics and Statistics, Xidian University, Xi'an, Shaanxi, P. R. China.

Journal of bioinformatics and computational biology
|January 22, 2024
PubMed
概括

这项研究引入了NMFTL,这是一种通过整合非负矩阵因子化和转移学习来赋值单细胞RNA测序 (scRNA-seq) 数据的新方法. NMFTL有效地解决了过度的零计数,改善了下游分析,如细胞聚类.

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) 为研究细胞异质性和转录组动态提供了高分辨率.
  • 在scRNA-seq数据中的一个重大挑战是零计数的高频率 (脱落事件),这阻碍了准确的下游分析.

研究的目的:

  • 为scRNA-seq数据开发一种先进的归算方法,克服过度零计数的局限性.
  • 为了提高基因表达估计的准确性,并保持数据中的生物关系.

主要方法:

  • 这项研究提出了一种新的方法,即非负矩阵因子化和转移学习 (NMFTL).
  • NMFTL集成非负矩阵因子化与转移学习,利用外部数据集来改善归算.
  • 将图形规范化术语纳入以保持数据的内在几何结构并提高归算准确性.

主要成果:

  • 与现有的基于矩阵因子化的归算方法相比,NMFTL在恢复缺失的基因表达值 (脱落条目) 中表现出更高的性能.
  • 该方法有效地保留了scRNA-seq数据中的基因对基因和细胞对细胞的关系.
  • 在下游分析中,NMFTL表现出强的表现,包括细胞聚类.

结论:

关键词:
单细胞RNA测序的测序方法归算是指指责一个人.非负矩阵因子化的非负矩阵因子化.转移学习转移学习

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

758
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

18.5K

相关实验视频

Last Updated: Jul 5, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
08:30

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

13.0K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

758
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

18.5K
  • NMFTL是一种强大而有效的方法,用于赋值scRNA-seq数据,显著改善现有技术.
  • 转移学习和图表规范化的整合提高了归算的基因表达数据的准确性和生物相关性.
  • 该NMFTL方法已在R脚本中实现,并为研究人员公开提供.