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RNA-seq03:21

RNA-seq

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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 12, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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协调的单细胞RNA-seq 细胞类型 辅助解卷 (HASCAD)

Yen-Jung Chiu1,2, Chung-En Ni1, Yen-Hua Huang3,4

  • 1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.

BMC medical genomics
|November 1, 2023
PubMed
概括

我们开发了HASCAD,这是一种用于细胞组成解卷 (CCD) 的深度神经网络模型,可以从大量RNA-seq数据中准确估计免疫细胞分数. 哈斯卡德的性能优于现有的方法,有助于理解免疫细胞异质性.

关键词:
细胞组成 解体 解体深度学习是一种深度学习.统一化 统一化 统一化在RNA-seqqq.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 细胞组成解 (CCD) 从大量基因表达数据中估计细胞分数.
  • 现有的CCD模型经常使用从单细胞RNA测序 (scRNA-seq) 来获得参考基因签名的线性回归.
  • 在scRNA-seq中,批量效应和脱落事件限制了当前CCD方法的性能.

研究的目的:

  • 开发一个深度神经网络 (DNN) 模型,HASCAD,用于准确的细胞组成解卷.
  • 从大量RNA-seq数据中预测多达15种免疫细胞类型的分数.
  • 评估HASCAD的性能与已建立的CCD方法相比.

主要方法:

  • 开发了HASCAD,这是一个DNN模型,其训练基于从规范化scRNA-seq数据集中获得的模拟散装RNA-seq数据.
  • 在scRNA-seq数据规范化中采用了Harmony-Symphony策略.
  • 使用模拟和人类PBMC RNA-seq数据集对比HASCAD与CIBERSORTx和quanTIseq.

主要成果:

  • 与CIBERSORTx和quanTIseq相比,HASCAD在分析大量RNA-seq数据方面表现优越.
  • 在参考scRNA-seq数据集中删除批量效应提高了CCD任务性能.
  • 对TCGA-LIHC肝癌数据的分析揭示了预测Treg和 CD8 T细胞丰度和患者存活率之间的关联.

结论:

  • 哈斯卡有效地从大量RNA-seq数据中预测免疫细胞组成.
  • 该模型有助于研究免疫细胞异质性对治疗反应的影响.
  • 在大量RNA-seq研究中,HASCAD是分析免疫细胞种群的宝贵工具.