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RNA-seq

RNA-seq

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...

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相关实验视频

Updated: Jan 14, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

scDETECT:一种新的统计模型,用于单细胞RNA-seq差异表达分析中的细胞类型相关性.

Yuhan Xu^1,2, Weiwei Zhang³, Hao Wu^1,4

¹Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, No. 1068 Xueyuan Avenue, Nanshan District, Shenzhen, Guangdong 518055, China.

Briefings in bioinformatics

|October 27, 2025

概括

我们开发了scDETECT,这是一种用于单细胞RNA测序 (scRNA-seq) 差异表达分析的新方法. 它考虑了细胞类型的相关性,与现有方法相比,提高了准确性和统计能力.

关键词:

贝叶斯的等级模型是贝叶斯的等级模型.细胞类型相关性相关性不同的表达方式,不同的表达方式.一个单细胞RNA-seqq.

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

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

Published on: September 18, 2021

相关实验视频

Last Updated: Jan 14, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes

Published on: November 7, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

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

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

Published on: September 18, 2021

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:

不同表达 (DE) 分析对于单细胞RNA测序 (scRNA-seq) 是至关重要的.
现有的DE方法往往忽略了细胞类型之间的相关性,降低了准确性和统计能力.
在scRNA-seq数据中的细胞类型经常表现出相关的表达模式.

研究的目的:

开发一种新的统计方法,scDETECT,用于scRNA-seq DE分析.
在DE分析中考虑细胞类型之间的相关性.
提高在scRNA-seq.中DE基因检测的准确性和统计能力.

主要方法:

开发了单细胞差异表达测试与细胞类型相关性 (scDETECT).
实现了贝叶斯的层次模型来整合细胞类型的相关性.
根据衍生后面概率,称为DE基因.

主要成果:

scDETECT有效地将细胞类型的相关性纳入DE分析.
模拟研究表明,改进了准确性和统计能力.
实际数据分析证实了scDETECT比现有方法的优势.

结论:

在scRNA-seq DE分析中,scDETECT提供了显著的进步.
考虑到细胞类型的相关性,提高了DE基因识别的可靠性.
该方法为scRNA-seq研究提供了更高的准确性和统计能力.