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

RNA-seq03:21

RNA-seq

10.4K
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...
10.4K
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

4.8K
4.8K
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

1.0K
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
1.0K
Experimental RNAi02:15

Experimental RNAi

6.3K
RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
6.3K

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

From Disease to Pregnancy: Rethinking Cardiac Remodeling Through Fibroblast, Immune Cell, and Hormonal Interactions.

Cells·2026
Same author

Panaxynol mitigates chemotherapy-induced intestinal mucositis by improving the colonic microenvironment in murine models.

American journal of physiology. Gastrointestinal and liver physiology·2026
Same author

Galectin7 attenuates abdominal aortic aneurysm progression by resisting disturbed flow induced endothelial-to-mesenchymal transition.

Theranostics·2026
Same author

Host-derived interleukin-1α drives tumor immunosuppression by reprogramming tumor-associated myeloid cells.

NPJ breast cancer·2026
Same author

Joint Modeling of Birth Outcomes Using a Copula Distributional Regression Approach.

Health economics·2025
Same author

Bivariate Copula-Based Regression for Joint Modeling of Healthcare Visits.

Health economics·2025

相关实验视频

Updated: Sep 12, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.6K

scCOSMIX:一种混合效应框架,用于在单细胞RNA-Seqq中对差异同表达和转录相互作用进行建模.

Anderson Bussing1, Giampiero Marra2, Daping Fan3

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.

Statistics in medicine
|August 7, 2025
PubMed
概括

我们开发了scCOSMiX,这是一个新的统计框架,用于分析单细胞RNA测序 (scRNA-seq) 数据中的基因相互作用. 这种方法可以考虑个体患者的数据,改善动态基因表达变化的研究.

关键词:
不同的共同表达差异化表达.层次化的研究设计设计.混合效应 混合效应 混合效应一个单细胞RNA-seqq.零膨胀的形模型模型.

更多相关视频

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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

Published on: March 7, 2018

13.7K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

38.5K

相关实验视频

Last Updated: Sep 12, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.6K
Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

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

Published on: March 7, 2018

13.7K
Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

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

Published on: September 18, 2021

38.5K

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) 提供了高分辨率的基因表达数据.
  • 了解动态的基因-基因相互作用对于细胞过程至关重要.
  • 现有的方法与多主体scRNA-seq数据的层次结构作斗争.

研究的目的:

  • 介绍scCOSMiX,一种新的混合效应框架,用于scRNA-seq.中的差异性共同表达分析.
  • 为了应对scRNA-seq数据中受试者细胞-细胞相关性的挑战.
  • 在复杂的实验设计中实现动态基因相互作用的强大建模.

主要方法:

  • 开发了一个基于copula的混合效果框架 (scCOSMiX).
  • 整合了主体级随机效应,以考虑个体内细胞相关性.
  • 模拟零通胀,边际和关联参数作为共变量的函数.

主要成果:

  • 与现有方法相比,scCOSMiX 在模拟研究中表现出优异的性能.
  • 该框架有效地模拟了有条件的共同表达变化.
  • 在多种不同的scRNA-seq协议 (基于滴滴和板块) 中显示的适用性.

结论:

  • scCOSMiX提供了一个强大的方法来分析scRNA-seq数据中的动态基因相互作用.
  • 该方法准确地处理了多主体实验的等级性质.
  • 这一框架增强了在单细胞分辨率下对基因-基因关系的系统研究.