曲阜师范大学学报（自然科学版）2026，Vol.52Issue(2)：65-73,9.DOI:10.3969/j.issn.1001-5337.202403.018
基于信息融合与图神经网络的单细胞测序数据插补模型
Single-cell sequencing data imputation model based on information fusion and graph neural network
摘要
Abstract
To address the problem of low gene expression counts and missing events faced by single-cell sequencing technologies,the paper proposes the model scFGImpute,a graph-convolution autoencoder that utilizes integrated distance and depth information fusion.Firstly,an integrated distance is used to construct a similarity network between cells,avoiding the bias and uncertainty of a single distance metric.Secondly,a graph attention convolutional structure aggregates similarity information from multi-layer neighboring cells,fully leveraging high-dimensional gene features and high-order cellular topology.Through a deep in-formation fusion network,a more complete consensus representation is generated.Experimental results demonstrate that compared to competing methods,scFGImpute can effectively utilize the original gene fea-ture information and the topological relationships between cells to uncover gene expression patterns hidden by noise from both global and local perspectives.It improves missing events,imputes excessive zero values,and reduces noise effects while maintaining robust and stable performance.关键词
单细胞RNA测序/缺失/插补/图神经网络Key words
single-cell RNA sequencing/dropout/imputation/graph neural network分类
信息技术与安全科学引用本文复制引用
王钰,倪建成,嵇存美..基于信息融合与图神经网络的单细胞测序数据插补模型[J].曲阜师范大学学报（自然科学版）,2026,52(2):65-73,9.基金项目
山东省自然科学基金重点项目(ZR2020KC022). （ZR2020KC022）
