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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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Updated: Jun 23, 2025

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

scHD4E:基于集体学习的新差异表达分析方法,用于单细胞RNA测序数据.

Biplab Biswas¹, Nishith Kumar², Masahiro Sugimoto³

¹Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh; Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.

Computers in biology and medicine

|June 19, 2024

概括

一种新的集体学习方法scHD4E增强了单细胞RNA测序 (scRNA-seq) 数据的差异表达分析. 它的性能优于现有的方法,利用高性能的个体分析,在复杂的生物数据集中获得更准确,更稳定的结果.

关键词:

不同表达式的差异表达式这就是为什么scDEA.scHD4E 在线播放这就是scRNA-seqq.

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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

Published on: November 11, 2014

相关实验视频

Last Updated: Jun 23, 2025

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

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Published on: July 29, 2022

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis

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科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

差异表达式 (DE) 分析对于scRNA-seq数据至关重要,但现有的方法在复杂的数据特征上扎.
集体学习方法,如scDEA,通过结合多种DE分析方法,已经显示出前景.

研究的目的:

引入scHD4E,一种基于集体学习的新型DE分析方法,用于scRNA-seq数据.
通过使用各种数据集,对scHD4E的性能与个别方法和scDEA进行评估.

主要方法:

scHD4E是通过对六个真实scRNA-seq数据集进行严格评估而确定的四种表现最好的DE分析方法的选择和组合来开发的.
在五个实验数据和一个模拟数据集上进行了全面的实验,以评估性能指标,包括样本大小效应,批量效应,I型错误控制和准确性.

主要成果:

在所有评估的视角上,scHD4E与单个DE分析方法和scDEA相比表现优越.
该方法显示了更好的准确性,F1得分和马修的相关系数,特别是在处理样本和批量效应方面.

结论:

scHD4E为在scRNA-seq数据中检测差异表达基因 (DEG) 提供了更准确,更稳定的解决方案.
为scHD4E提供了一个R包和Shiny应用程序,以促进数据科学家的采用.