广东工业大学学报2025，Vol.42Issue(5)：129-136,8.DOI:10.12052/gdutxb.240018
基于自适应图正则化低秩表示的scRNA-seq数据分析方法
ScRNA-seq Data Analysis Based on Adaptive Graph Regularization and Low-rank Representation
摘要
Abstract
Single-cell RNA sequencing(scRNA-seq)can be used to study the gene expression of single cell and generate a large amount of single-cell gene expression data.This type of data generally has high-dimensional and complex structures,requiring dimension reduction and clustering analysis to reveal differences between cell types and states.A new scRNA-seq data analysis method(scLRRAGR)is proposed based on adaptive graph regularization low-rank representation.This method can fully utilize the global and local information of scRNA-seq data for graph learning,and capture the similarity and interaction between cells by adaptive graph regularization and the introduction of rank constraint.Its outcome can better reflect the clustering structure between cells and help to reveal differences between different cell types and states.When applying this method,scRNA-seq data can be transformed into a graph structure with each node representing a single-cell sample and edges representing similarities or interactions between cells.Then this method is used to learn and optimize this graph to obtain the optimal graph representation.Finally,typical clustering algorithms can use the optimal graph representation to recognize cell types and states.The experiment results show that the proposed method can significantly improve clustering performance on scRNA-seq datasets.关键词
scRNA-seq数据/细胞聚类/图正则化/低秩表示/秩约束Key words
scRNA-seq data/cell clustering/graph regularization/low-rank representation/rank constraint分类
生物科学引用本文复制引用
冯思凡,王振友,金应华..基于自适应图正则化低秩表示的scRNA-seq数据分析方法[J].广东工业大学学报,2025,42(5):129-136,8.基金项目
广东省自然科学基金资助项目(2023A1515012891) （2023A1515012891）
