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

One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

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

Non-Mendelian inheritance of DNA methylation patterns in mice.

Nature genetics·2026
Same author

SMARCB1 missense mutants disrupt SWI/SNF complex stability and remodeling activity.

Nature communications·2026
Same author

BAF complexes maintain accessibility at stimulus-responsive chromatin and are required for transcriptional stimulus responses.

bioRxiv : the preprint server for biology·2026
Same author

Spatially resolved molecular sex differences at single-cell resolution in the adult human ventromedial and arcuate hypothalamus.

Cell reports·2026
Same author

Orchestrating Spatial Transcriptomics Analysis with Bioconductor.

bioRxiv : the preprint server for biology·2025
Same author

Generation and characterization of a knockout mouse of an enhancer of EBF3.

Biology open·2025

相关实验视频

Updated: Jul 9, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
22:27

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

408.9K

在高温实验中消除样品之间的不必要变异.

Kipper Fletez-Brant1,2, Yunjiang Qiu3,4, David U Gorkin4,5,6

  • 1McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.

Briefings in bioinformatics
|May 7, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了带智能规范化和批量校正,以改善不同样本中Hi-C数据的比较. 该方法有效地减少了技术变化,增强了下游分析,如QTL映射和差异缩研究.

关键词:
这就是Hi-C.生物信息学是一种生物信息学.

更多相关视频

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
10:16

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

32.4K
Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

3.4K

相关实验视频

Last Updated: Jul 9, 2026

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
22:27

Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.

Published on: May 6, 2010

408.9K
Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions
10:16

Promoter Capture Hi-C: High-resolution, Genome-wide Profiling of Promoter Interactions

Published on: June 28, 2018

32.4K
Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

3.4K

科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 高C数据分析通常使用单个样本规范化,限制交叉样本比较.
  • 在Hi-C数据中存在不必要的技术变化,特别是在不同个体之间.
  • 这种变异的特征在接触地图中的基因组之间可能有所不同.

研究的目的:

  • 开发和验证一种用于规范化和批量纠正Hi-C数据的方法,以实现可靠的交叉样本比较.
  • 为了应对来自不同个体的Hi-C复制品技术变化的挑战.
  • 为了提高下游分析的准确性,这些分析依赖于比较多个Hi-C接触图.

主要方法:

  • 为Hi-C数据开发一种新的带智能规范化和批量校正方法.
  • 该方法应用于来自不同个体的Hi-C数据集.
  • 评估该方法在改善交叉样本比较方面的表现.

主要成果:

  • 在样本中的Hi-C数据中证明了技术变化的存在和变化性质.
  • 展示了带式规范化和批量校正可以显著减少不必要的变化.
  • 在交叉样本比较方面得到了实质性的改进,包括定量特征位置 (QTL) 分析和差异性丰富性研究.

结论:

  • 带式规范化和批量校正是跨多个样本的Hi-C数据分析的有效策略.
  • 该方法提高了对Hi-C接触地图进行比较的可靠性,从而获得更准确的生物见解.
  • 这种方法对于研究不同个体或条件的3D基因组组织变异至关重要.