Yuhan Fu, Mengdi Nan, Qing Ren et al. (5 total)
2025-04-01
Bioinformatics Vol. 41
10.1093/bioinformatics/btaf172
摘要
Abstract Motivation Spatial transcriptomics (ST) addresses the loss of spatial context in single-cell RNA-sequencing by simultaneously capturing gene expression and spatial location information. A critical task of ST is the identification of spatial domains. However, challenges such as high noise levels and data sparsity make the identification process more difficult. Results To tackle these challenges, STMGAMF, a multi-view graph convolutional network model that employs an adaptive adjacency ma...
空间转录组学（ST）分析中的关键挑战是空间域的识别，但高噪声和数据稀疏性使其变得困难。本研究旨在开发一种能够有效应对这些挑战的算法，以提高空间域识别的准确性和稳定性。
本研究提出了一种多视图图卷积网络模型STMGAMF，该模型采用自适应邻接矩阵和多策略融合机制。STMGAMF动态调整边缘权重，并通过多策略融合机制优化嵌入特征。
STMGAMF在多个ST数据集上进行了评估，并在空间域识别、可视化和空间轨迹推断等任务中优于现有算法。结果表明，STMGAMF在空间域识别方面表现出色，具有强大的泛化能力。