Search research articles

相关概念视频

Cell Specific Gene Expression

Cell Specific Gene Expression

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

Revealing tissue architecture through the hypercomplex Fourier analysis of spatial transcriptomics data.

Bioinformatics advances·2025

Same author

Gene set optimization for cancer transcriptomics using sparse principal component analysis.

bioRxiv : the preprint server for biology·2025

Same author

Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.

bioRxiv : the preprint server for biology·2025

Same author

Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.

bioRxiv : the preprint server for biology·2024

Same author

Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error.

PLoS computational biology·2024

Same author

Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.

PLoS computational biology·2024

Same journal

DeepScope: Nonintrusive Whole Slide Saliency Annotation and Prediction from Pathologists at the Microscope.

Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting)·2018

查看所有相关文章

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

Search research articles

相关实验视频

Updated: Jun 13, 2025

ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

为单细胞转录组学优化基因组.

H Robert Frost¹

¹Dartmouth College, Hanover NH 03755, USA.

Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting)

|June 9, 2025

概括

此摘要是机器生成的。

分析单细胞RNA测序 (scRNA-seq) 数据是困难的. 这项研究提出了一种定制基因组的方法,用于细胞类型特定的分析,提高统计能力和scRNA-seq数据的解释.

关键词:

细胞类型特异性的细胞类型特异性.基因组优化优化基因组优化基因组测试是基因组测试.路径分析路径分析单细胞转录学转录学

更多相关视频

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

相关实验视频

Last Updated: Jun 13, 2025

ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

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) 提供了深刻的生物学见解,但也带来了分析挑战,比如低统计能力和复杂的数据特征.
现有的基因组集合,设计用于散装组织分析,由于固有的数据差异,包括噪声和稀疏性,对scRNA-seq来说不是最佳的.
基因组测试 (途径分析) 是解决这些挑战的有希望的方法,但需要适应单细胞数据.

研究的目的:

开发一种定制现有基因组集合的程序,用于scRNA-seq数据的细胞类型特定分析.
提高scRNA-seq研究中的基因组测试的统计能力和可解释性.
为了利用人类细胞类型特定的基因表达数据来改进途径分析.

主要方法:

通过计算细胞类型特定的基因和基因组重量来定制基因组集合.
使用来自人类蛋白质图谱 (HPA) 单细胞类型图谱的平均基因表达数据,分析了81种人类细胞类型.
应用细胞类型特定权重来过或调整用于scRNA-seq分析的基因组集合.

主要成果:

在基因组测试能力和可解释性方面显著改善.
成功地应用了该方法来分析免疫细胞scRNA-seq数据,使用来自分子签名数据库 (MSigDB) 的基因组.
展示了在途径分析中考虑细胞类型特异性的有效性.

结论:

根据细胞类型特定的表达模式定制基因组集合对于有效的scRNA-seq分析至关重要.
开发的程序增强了对单细胞转录组数据的途径分析的实用性.
这种方法为更深入地解释复杂单细胞实验的生物学解释提供了强大的工具.