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结合全球受约束概念因子化和规范化的高斯图形模型,用于集群单细胞RNA-seq数据

Yaxin Xu¹, Wei Zhang², Xiaoying Zheng³

¹School of Sciences, East China Jiaotong University, Nanchang, 330013, China.

Interdisciplinary sciences, computational life sciences

|October 10, 2023

概括

聚类单细胞RNA测序 (scRNA-seq) 数据由于其复杂性而具有挑战性. GCFG是一种新的计算方法,通过整合全球和本地信息,有效地集群scRNA-seq数据,在多个数据集中显示出强大的性能.

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) 产生了大量的数据,揭示了细胞异质性.
聚类scRNA-seq数据对于下游分析至关重要,但由于高维度,噪音,稀疏性和缺失数据而具有挑战性.
现有的计算方法通常效率不足,需要预先指定的集群号码,限制了它们在现实世界中的适用性.

研究的目的:

开发一种新的计算方法,用于准确和强大的scRNA-seq数据的聚类.
为了解决现有方法的局限性,特别是对预定义集群号的需求.
在scRNA-seq数据集中有效地利用全球和本地信息.

主要方法:

开发了GCFG,这是一个计算方法,集成了全球数据属性的概念因子化和局部嵌入关系的规范化高斯图形模型.
设计了一种代优化算法,通过结合全球和本地信息来学习细胞-细胞相似性矩阵.
在单细胞分类的学习相似性矩阵上利用了卢温社区发现算法.

主要成果:

在14个现实世界scRNA-seq数据集上评估了GCFG.
使用准确度 (ACC) 和调整后的Rand指数 (ARI) 来评估性能.
与其他17种竞争性集群方法相比,GCFG表现出卓越的有效性和稳定性.

结论:

GCFG提供了一种有效和强大的方法来对scRNA-seq数据进行聚类.
该方法成功地整合了全球和本地数据特征,以改进分类.
GCFG克服了现有方法的局限性,为转录组异质性分析提供了有价值的工具.

关键词:

集群集成是指集群集成.概念因子化是一个概念.图形模型的图形模型.的scRNA-seq数据.

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