ScRNA-seq Data Analysis Based on Adaptive Graph Regularization and Low-rank Representation

Feng Sifan; Wang Zhenyou; Jin Yinghua

doi:10.12052/gdutxb.240018

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ScRNA-seq Data Analysis Based on Adaptive Graph Regularization and Low-rank Representation

更新时间：2026-05-27

- ScRNA-seq Data Analysis Based on Adaptive Graph Regularization and Low-rank Representation
- Vol. 42, Issue 5, Pages: 129-136(2025)
- 作者机构：
  
  广东工业大学数学与统计学院,广东,广州,510520
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240018
  CLC： Q332
- Published：2025
- 稿件说明：
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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.

Feng Sifan, Wang Zhenyou, Jin Yinghua. ScRNA-seq Data Analysis Based on Adaptive Graph Regularization and Low-rank Representation[J]. 2025, 42(5): 129-136.
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. DOI： 10.12052/gdutxb.240018

Feng Sifan, Wang Zhenyou, Jin Yinghua. ScRNA-seq Data Analysis Based on Adaptive Graph Regularization and Low-rank Representation[J]. 2025, 42(5): 129-136. DOI： 10.12052/gdutxb.240018.

摘要

单细胞RNA测序(scRNA-seq) 技术可以用于研究单个细胞的基因表达情况，生成大量的单细胞基因表达数据。这类数据通常具有高维度和复杂的结构，需要进行降维和聚类分析来揭示细胞类型和状态之间的差异。本文提出了一种基于自适应图正则化低秩表示的scRNA-seq数据分析方法——scLRRAGR。该方法可以充分利用scRNA-seq数据的全局和局部信息进行图学习，并通过自适应图正则化和引入秩约束来捕捉细胞之间的相似性和相互作用，更好地反映细胞之间的聚类结构，帮助揭示不同细胞类型和状态之间的差异。在应用该方法时，可以将scRNA-seq数据转换为一个图结构，其中每个节点表示一个单细胞样本，边表示细胞之间的相似性或相互作用。然后使用该方法对图进行学习和优化，得到最优的图表示。最后，可以使用聚类算法将学习到的图表示应用于细胞类型和状态的识别。实验结果表明，本文方法应用在scRNA-seq数据集上能够显著提高聚类性能。

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.

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references

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