单细胞RNA测序数据分析的进展和挑战:综合性综述
Ali Mohammad Nesari1, Habib MotieGhader1, Saeid Ghorbian1
1Department of Biology, Ta.C., Islamic Azad University, Tabriz, Iran.
Briefings in bioinformatics
|January 31, 2026
概括
本综述强调了单细胞RNA测序 (scRNA-seq) 的计算进步,以克服临床使用的数据挑战. 新兴的工具改善了数据预处理,单元注释和多式联通,为诊断铺平了道路.
科学领域:
- 计算生物学是一种计算生物学.
- 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测序 (scRNA-seq) 为生物过程提供了对细胞异质性的高分辨率见解.
- 由于数据稀疏,批量效应和缺乏标准化的基准,scRNA-seq的临床翻译受到阻碍.
- 像Seurat和Scanpy这样的现有管道需要强大的计算策略来实现可靠的临床应用.
研究的目的:
- 审查新兴的计算策略,以解决scRNA-seq在临床翻译方面的局限性.
- 评估基于变压器的注释工具和多式联接与空间转录学.
- 提出临床采用路线图,包括基准工作流程和隐私意识的数据共享.
主要方法:
- SCTransform用于零通胀纠正和Harmony用于批量集成.
- 基于变压器的注释工具 (scGPT,CellTypist) 用于免疫分析.
- 多模式集成与空间转录学 (10x Visium,cell2location v2) 和scANVI用于表观遗传分析.
主要成果:
- 对于大型队伍,Harmony实现了比BBKNN快30%的对齐速度.
- 基于变压器的工具在免疫分析中达到>95%的准确性.
- 空间方法在亚细胞分辨率下划分微环境和瘤免疫交叉声.
结论:
- 计算策略,如强大的预处理,先进的注释和多模式集成,对于临床scRNA-seq.至关重要.
- 解决伦理风险和建立基准工作流程对于广泛采用至关重要.
- 因果AI和联合学习可以增强scRNA-seq应用中的数据分析和隐私.
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