Search research articles

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

相关概念视频

Overview Of Cell Separation And Isolation

Overview Of Cell Separation And Isolation

Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.

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

CrossIsoFun: predicting isoform functions using the integration of multi-omics data.

Bioinformatics (Oxford, England)·2024

Same author

LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.

Briefings in bioinformatics·2024

Same author

scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.

Nature communications·2024

Same author

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Bioinformatics (Oxford, England)·2024

Same author

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

Bioinformatics (Oxford, England)·2023

Same author

Computational approaches for detecting disease-associated alternative splicing events.

Briefings in bioinformatics·2023

Search research articles

相关实验视频

Updated: Jul 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

FEED:一种基于基因表达分解的特征选择方法,用于单细胞聚类.

Chao Zhang¹, Zhi-Wei Duan¹, Yun-Pei Xu¹

¹School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China.

Briefings in bioinformatics

|November 7, 2023

概括

此摘要是机器生成的。

使用基因表达分解 (FEED) 的特征选择改善了单细胞RNA测序 (scRNA-seq) 数据集群. 这种方法通过分析基因表达分布来提高细胞类型识别的准确性.

关键词:

基因表达的基因表达方式基因选择基因选择这就是scRNA-seqq.单细胞聚类的单细胞聚类.

更多相关视频

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

相关实验视频

Last Updated: Jul 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

科学领域:

计算生物学计算生物学
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:

单细胞RNA测序 (scRNA-seq) 分析依赖于准确的聚类来进行下游生物解释.
目前的特征选择方法经常忽视基因表达分布和群体内部异质性,限制了聚类性能.
鉴定细胞类型特定的基因对于提高单细胞数据分析的精度至关重要.

研究的目的:

引入一种新的特征选择方法,基于基因表达分解 (FEED) 的特征选择,用于scRNA-seq数据.
通过选择信息基因,提高scRNA-seq数据集中细胞类型识别的准确性.
通过结合基因表达分布和异质性来解决现有方法的局限性.

主要方法:

将基因表达水平分解为多个高斯元件.
一种基于表达分布的新型基因相关性计算方法.
一种基于变的方法来确定标记基因子集选择的基因重要性值.

主要成果:

在各种scRNA-seq数据集 (包括大规模数据集) 中,FEED显著提高了细胞类型识别的准确性.
与最先进的特征选择技术相比,该方法显示出更高的性能.
应用FEED,然后使用共同的集群算法,始终会产生更好的结果.

结论:

在scRNA-seq数据分析中,FEED提供了一种强大的特征选择方法.
该方法有效地利用基因表达分布来识别信息基因.
FEED提高了单细胞数据集群和细胞类型识别的可靠性和准确性.