Search research articles

相关概念视频

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...

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

Preliminary Comparative Analysis of Circulatory Glycosaminoglycan Concentrations and Disaccharide Profiles in Diabetic and Healthy Subjects.

The Eurasian journal of medicine·2026

Same author

Characteristics of asymmetric microcrystalline solidification pellets and a better prediction for bioequiavailability based on solubility-permeability theory.

International journal of pharmaceutics·2026

Same author

Coronavirus M Protein Hijacks Toll-Interacting Protein (TOLLIP) to Suppress NF-κB Signaling and Promote Immune Evasion.

MedComm·2026

Same author

Multimodal Machine Learning Integrating Clinical and Proteomic Data for Early Prediction of Hypertensive Complications: A UKB Longitudinal Study.

Journal of the American Heart Association·2026

Same author

Deep Prior Framework: integrating functional specificity with general plausibility for targeted protein evolution.

Briefings in bioinformatics·2026

Same author

Development and Validation of a Novel Risk Prediction Model for Incident Diabetic Foot Complications: A Large, Longitudinal, Population-Based Machine Learning Study.

Advances in wound care·2026

Same journal

High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.

Genome research·2026

Same journal

Complete sequencing of medaka genomes reveals the architecture of centromeric satellites, giant mobile elements, and sex chromosomes.

Genome research·2026

Same journal

Convergence and conflict among telomere specialized transposons across 60 million years of Drosophilid evolution.

Genome research·2026

Same journal

A unified analysis of cell type- and trajectory-associated pathways in single-cell data using Phoenix.

Genome research·2026

Same journal

Resf1 is required for proper placental development and configuration of trophoblast cell-specific heterochromatin.

Genome research·2026

Same journal

Telomere-driven replicative crisis is driven by large-scale changes in genomic architecture.

Genome research·2026

查看所有相关文章

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

Search research articles

相关实验视频

Updated: May 14, 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

STCC通过空间转录学数据的共识聚类来增强空间域检测.

Congcong Hu¹, Nana Wei^2,3, Jiyuan Yang¹

¹Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Genome research

|May 12, 2025

概括

此摘要是机器生成的。

空间转录学中的空间域检测对于生物学见解至关重要. 我们的STCC框架使用共识聚类来提高准确性和稳定性,通过整合多种分析工具,优于单个方法.

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

相关实验视频

Last Updated: May 14, 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

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

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:

空间解析的转录学产生复杂的数据,需要强大的分析.
空间域检测 (集群) 是生物学解释的一个基本步骤.
现有的空间域检测工具在数据集和平台上显示了可变的性能.

研究的目的:

开发一个新的共识集群框架 (STCC) 用于空间转录学数据.
通过汇总多种工具的结果来提高空间域检测的准确性和稳定性.
评估不同数据集上的共识聚类策略的性能.

主要方法:

开发了STCC,这是空间转录组学的共识集群框架.
集成的最先进的工具使用各种共识策略 (基于Onehot,基于平均值,基于超图,基于wNMF).
对来自不同实验平台的模拟和现实数据进行了全面评估.

主要成果:

与各种参数的个体方法相比,共识聚类显著提高了聚类准确性.
在整合多种方法时,STCC在具有多层结构的正常组织中表现出更好的结果.
对于具有分散模式的瘤样本,整合单一基线方法提供了令人满意的性能.
基于平均值和基于超图的共识策略显示出最佳的精度和稳定性.

结论:

在空间转录学中,STCC为空间域检测提供了一个可扩展和实用的解决方案.
共识聚类改善了从空间转录组学数据中获得生物学洞察力.
该框架为未来该领域的研究和应用提供了基础.