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

Deconvolution01:20

Deconvolution

764
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
764

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

Construction of a public health emergency information system framework: A case study of Zhuhai city, China.

PloS one·2026
Same author

Differential nucleosome organization in human interphase and metaphase chromosomes.

Molecular systems biology·2026
Same author

Robust Detection of Watermarks for Large Language Models Under Human Edits.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same author

Differential nucleosome organization in human interphase and metaphase chromosomes.

bioRxiv : the preprint server for biology·2025
Same author

A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules.

Annals of statistics·2025
Same author

DDX6 interacts with DDX3X to repress translation in microRNA-mediated silencing.

Nucleic acids research·2025
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
查看所有相关文章

相关实验视频

Updated: May 2, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.7K

NLSDeconv:一种高效的细胞类型解卷方法,用于空间转录组学数据.

Yunlu Chen1, Feng Ruan1, Ji-Ping Wang1

  • 1Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States.

Bioinformatics (Oxford, England)
|December 20, 2024
PubMed
概括
此摘要是机器生成的。

一种名为NLSDeconv的新方法通过准确估计细胞类型来增强空间转录学 (ST). 与现有的解卷方法相比,这种计算工具提供了更高的性能和效率.

更多相关视频

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

2.9K

相关实验视频

Last Updated: May 2, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

13.7K
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

4.8K
Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
09:45

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

2.9K

科学领域:

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

背景情况:

  • 空间转录组学 (ST) 在完整的组织中提供基因表达数据.
  • ST缺乏单细胞分辨率,需要计算解卷.
  • 准确的细胞类型解对于解释ST数据至关重要.

研究的目的:

  • 介绍NLSDeconv,一种基于非负最小平方的新型解卷方法.
  • 为NLSDeconv.开发一个配套的Python包.
  • 评估NLSDeconv的性能与现有方法相比.

主要方法:

  • 开发了使用非负最小平方的NLSDeconv.
  • 作为一个Python包实现了NLSDeconv.
  • 在各种ST数据集上对比NLSDeconv与其他18种解卷方法.

主要成果:

  • NLSDeconv表现出具有竞争力的统计性能.
  • NLSDeconv表现出卓越的计算效率.
  • 该方法在各种空间转录组学数据集中被证明是有效的.

结论:

  • NLSDeconv是空间转录学中细胞类型解卷的高效和准确工具.
  • 该 NLSDeconv Python 包为研究人员提供了一个宝贵的资源.
  • 这种方法推进了空间转录学数据的分析.