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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 7, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

一种基于信号扩散的无监督对比表示学习,用于空间转录学分析.

Nan Chen¹, Xiao Yu¹, Weimin Li¹

¹School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.

Bioinformatics (Oxford, England)

|November 15, 2024

概括

此摘要是机器生成的。

我们开发了一种新的方法,SDUCL,通过整合基因表达,空间信息和图像来更好地分析空间转录组数据. 这种方法改善了对组织异质性和瘤微环境的理解.

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相关实验视频

Last Updated: Jun 7, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Author Spotlight: Exploring Advanced Therapeutic Targets in Osteosarcoma Through Spatial Transcriptomics

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Published on: May 3, 2024

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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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:

空间转录学使得高通量基因表达测量与保存的组织空间结构.
整合基因表达,空间和图像数据对于剖析组织异质性和生物功能至关重要.
现有的方法很难有效地利用空间信息和高分辨率组织学图像.

研究的目的:

提出一种基于信号扩散的新型无监督对比学习方法 (SDUCL),用于学习细胞/斑点的低维潜伏嵌入.
改进空间信息和组织学图像在空间转录学分析中的整合.
通过改进数据整合,增强对组织异质性和生物功能的理解.

主要方法:

SDUCL集成图像特征,空间关系和基因表达数据.
信号扩散微环境发现算法模拟生物信号扩散以捕捉细胞微环境相互作用.
当地和微环境代表之间的相互信息最大化学习了歧视性嵌入.

主要成果:

SDUCL有效地集成多模式数据,包括图像特征,空间关系和基因表达.
信号扩散算法捕获了细胞微环境的相互作用.
SDUCL通过最大化相互信息来学习更多的歧视性表示.
对各种空间转录组数据集 (多种,正常,瘤) 的分析表明了SDUCL的有效性.

结论:

SDUCL增强了下游任务,如集群,可视化,轨迹推断和差异基因分析.
该方法提高了对组织结构和瘤微环境的理解.
SDUCL提供了一个强大的新工具,用于空间转录学数据分析.