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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: Jun 30, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

通过跳过聚合网络进行有效的多模式集群方法,用于并行scRNA-seq和scATAC-seq数据.

Dayu Hu¹, Ke Liang¹, Zhibin Dong¹

¹School of Computer, National University of Defense Technology, No. 109 Deya Road, 410073 Changsha, Hunan, China.

Briefings in bioinformatics

|March 17, 2024

概括

这项研究介绍了scEMC,这是一种用于单细胞RNA-seq (scRNA) 和scATAC数据的新型多模式集群模型. scEMC有效地整合了这些模式,改善了细胞亚群和瘤微环境分析.

关键词:

在ZINB,你会发现.深度学习是一种深度学习.拒绝使用自动编码器.单细胞聚类的单细胞聚类.跳过聚合网络网络的跳过.

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Adipocyte-Specific ATAC-Seq with Adipose Tissues Using Fluorescence-Activated Nucleus Sorting

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Last Updated: Jun 30, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

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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-seq (scRNA) 和单细胞转移酶可访问染色体 (scATAC) 数据的并行聚类变得越来越重要.
与scATAC相比,现有的方法往往无法利用scRNA数据中的更丰富的信息,从而限制了性能.
这可能会损害细胞亚群和瘤微环境的准确识别.

研究的目的:

提出一个有效的多模式集群模型,scEMC,用于并行 scRNA 和 scATAC 数据集成.
为了解决scRNA和scATAC数据在聚类中的信息不平衡.
增强细胞亚群和瘤微环境的分析.

主要方法:

开发了一个跳过聚合网络,用于同时学习全球细胞结构和多模式集成.
从scRNA数据实现跳过连接,以保护对稀疏scATAC数据的表示质量.
使用基于零膨胀负二进制的否定自动编码器和具有多次损失的联合优化模块.

主要成果:

scEMC模型在多模式集群任务中表现出显著的有效性.
实验结果强调了该模型在整合多种单细胞数据模式方面的能力.
该方法成功地改善了复杂生物系统的分析.

结论:

scEMC通过处理模式特定的信息内容,为scRNA和scATAC数据的并行聚类提供了有效的解决方案.
该模型有助于精确识别细胞亚群和描述瘤微环境.
开发的方法有助于更广泛的单细胞多组体分析领域.