使用网络集群算法进行细胞类型分化
Fatemeh Sadat Fatemi Nasrollahi1, Filipi Nascimento Silva1, Shiwei Liu2
1Observatory of Social Media, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indiana, USA.
bioRxiv : the preprint server for biology
|December 16, 2024
概括
在单细胞RNA测序 (scRNA-seq) 中,准确的细胞类型注释至关重要. Infomap和莱登集群算法有效地从基因表达数据中识别细胞类型,在生物洞察方面表现优于WGCNA.
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:
- 单细胞RNA测序 (scRNA-seq) 提供高分辨率的转录组数据.
- 准确的细胞类型注释是scRNA-seq数据分析中的一个关键挑战.
- 在-算法对于理解细胞异质性和疾病机制至关重要.
研究的目的:
- 在scRNA-seq数据中比较用于细胞类型识别的各种算法的性能.
- 分析和优化用于scRNA-seq分析的预处理管道.
- 评估从基因表达数据中获得的细胞-细胞网络上的聚类方法.
主要方法:
- 对聚类算法的比较分析:Seurat,Leiden,WGCNA,Infomap,随机块模型 (SBM) 和单细胞图形神经网络 (scGNN).
- 对scRNA-seq数据的预处理管道的分析.
- 将聚类算法应用于从PBMC和ROSMAP数据集衍生出来的细胞-细胞网络.
主要成果:
- 通过WGCNA进行的聚类显示,与已知的细胞类型的对应性有限.
- 多解析度的Infomap,Leiden和SBM算法与细胞类型更接近.
- Infomap已经成为一种非常有效的细胞类型识别方法.
结论:
- Infomap和莱登算法在scRNA-seq数据中提供了强大的细胞类型注释.
- 这些方法在神经退行和免疫学中对细胞景观的特征有价值.
- 优化预处理和聚类是释放scRNA-seq潜力的关键.
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