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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: Jul 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

用多个单细胞RNA-seq样本进行差异化伪时间分析的统计框架.

Wenpin Hou^1,2, Zhicheng Ji^1,3, Zeyu Chen^4,5,6,7

¹Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.

Nature communications

|November 10, 2023

概括

此摘要是机器生成的。

拉米安是一个新的计算框架,用于使用单细胞RNA测序 (scRNA-seq) 数据在多个样本中分析生物过程的差异. 它准确地识别了生物变化,同时考虑了样本的变化,减少了错误发现.

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

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Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

相关实验视频

Last Updated: Jul 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
单细胞分析单细胞分析

背景情况:

对单细胞RNA测序 (scRNA-seq) 数据的伪时间分析对于理解动态基因调控程序至关重要.
现有的方法难以在多个样本和实验条件中比较伪时空模式.
在样本中比较伪时空模式对于强大的生物发现至关重要.

研究的目的:

介绍Lamian,一个新的计算框架,用于差异化多样本伪时分析.
为了使生物过程在不同样本和条件下进行统计严格的比较.
在多样本环境中识别基因表达,细胞密度和轨迹拓的变化.

主要方法:

开发Lamian,一个全面的计算框架,用于差异化多样本伪时分析.
统计推断对交叉样本变异性的考虑,以减少错误发现.
应用于真实scRNA-seq数据和模拟数据,包括COVID-19患者的免疫反应.

主要成果:

拉米安确定了与样本共变量 (例如疾病严重程度) 相关的生物过程的变化.
该框架有效地调整了多样本scRNA-seq数据中的批量效应.
与现有方法相比,拉米安证明了较少的样本特定错误发现.

结论:

拉米安为差异多样本假名时间分析提供了一个统计严格的方法.
该框架增强了在连续生物过程中解码细胞基因表达程序的能力.
拉米安对于在多个样本和条件中分析动态生物过程具有优势.