Jove
Visualize
联系我们
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

相关概念视频

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
RNA-seq03:21

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 microarray-based...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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 author

SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.

Bioinformatics (Oxford, England)·2026
Same author

Imaging-based organ-specific aging clock predicts human diseases and mortality.

NPJ digital medicine·2026
Same author

Lifestyle-Associated Metabolic Signature Predicts the Risk of Amyotrophic Lateral Sclerosis.

Muscle & nerve·2026
Same author

DeepRMSF: a deep learning-based automated approach for predicting atomic-level flexibility in RNA structure.

Briefings in bioinformatics·2026
Same author

scGeneBank: cross-species screening of functional gene sets at single-cell resolution.

Nucleic acids research·2025
Same author

scBrainScope: cross-species multidimensional brain atlas.

Nucleic acids research·2025
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 25, 2026

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq
07:49

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

9.5K

scValue:用于机器和深度学习任务的大规模单细胞转录组数据的基于值的分样采集.

Li Huang1, Weikang Gong1,2, Dongsheng Chen1

  • 1State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, 100 Chongwen Road, Suzhou Industrial Park, Suzhou, Jiangsu Province 215123, China.

Briefings in bioinformatics
|June 14, 2025
PubMed
概括
此摘要是机器生成的。

scValue是一种用于分样大单细胞RNA测序 (scRNA-seq) 数据集的新方法. 它优先考虑高价值的细胞,改进机器学习和深度学习任务,同时保持生物信号.

关键词:
细胞类型分析细胞类型分析数据估值数据的估值.机器学习和深度学习.单细胞转录组学 单细胞转录组学部分采样采样

更多相关视频

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
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

663

相关实验视频

Last Updated: Jun 25, 2026

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq
07:49

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

9.5K
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
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

663

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 大型单细胞RNA测序 (scRNA-seq) 数据集提供了深刻的生物学见解,但也带来了重大的计算挑战.
  • 现有的部分采样技术可以提高效率,但可能会损害下游机器学习和深度学习 (ML/DL) 分析的性能.

研究的目的:

  • 介绍scValue,一种新的细胞排名方法,用于高效有效地对大型scRNA-seq数据进行分样.
  • 通过保护关键的生物信号和改善亚样本中的细胞类型表示来增强ML/DL工作流.

主要方法:

  • 开发了scValue,该方法根据"数据值"对单元格进行排名,使用随机森林的袋外估计.
  • 优先考虑的高值细胞和过量采样的细胞类型,具有更大的数据值可变性.
  • 在细胞类型注释,标签转移学习,交叉研究标签协调和大量RNA-seq解卷任务上进行基准 scValue.

主要成果:

  • 在自动单元格类型注释任务中,scValue的表现始终优于现有的部分采样方法,实现了接近完整数据分析的性能.
  • 在案例研究中证明了T细胞注释的优越保存和T细胞亚型关系的准确复制.
  • 在16个公共数据集上进行评估,scValue显示出快速执行,平衡的细胞类型表示和类似于统一抽样的分布性质.

结论:

  • 在ML/DL应用中,scValue提供了一种强大且可扩展的解决方案,用于在ML/DL应用中进行大型scRNA-seq数据集的分样.
  • 该方法有效地保护了生物信号,并提高了下游分析的性能.
  • scValue可以作为一个开源的Python包.