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关于对不同类型的单细胞RNA测序数据进行聚类的双聚类和聚类方法的调查

Chaowang Lan¹, Xiaoqi Tang¹, Caihua Liu¹

¹School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China.

Briefings in functional genomics

|August 12, 2025

概括

这项研究调查了单细胞RNA测序 (scRNA-seq) 数据的无监督双聚类和聚类方法. 它发现双聚类优于部分注释,而聚类对未知数据集更好,有助于细胞亚型识别.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

单细胞RNA测序 (scRNA-seq) 为细胞异质性提供了高分辨率.
像聚类和双聚类这样的无监督方法对于分析scRNA-seq数据至关重要.
在复杂的scRNA-seq数据集中确定精确的细胞亚型仍然是一个重大挑战.

研究的目的:

评估scRNA-seq数据分析的无监督方法的现状.
总结双重集群和集群方法的优点,弱点和改进策略.
为根据数据集特征选择合适方法提供指导方针.

主要方法:

调查了5个双聚类和21个聚类算法,应用于scRNA-seq数据.
在10个现实数据集上使用3个外部和2个内部指标评估方法性能.
从六个角度量化数据集属性,以确定方法的适用性.

主要成果:

双聚类方法对于局部一致性识别和挖掘部分注释的数据集是有效的.
聚类方法更适合分析未知的数据集.
方法性能根据数据集属性大大不同.

结论:

双重集群和集群之间的选择取决于数据集特征和研究目标.
这项调查提供了选择最佳方法以确定细胞异质性的建议.
未来的研究应该专注于开发针对特定scRNA-seq数据挑战的可靠方法.

关键词:

双聚类是指双聚类.集群集成是指集群集成.数据集属性数据集属性这就是 scRNA-seqq.

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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...