Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks - Feng, Xiang; Fang, Fang; Long, Haixia; Zeng, Rao; Yao, Yuhua (Frontiers in Genetics, 2022)
With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data is also Surg. Its data is typical high-dimensional, with high dropout noise and high sparsity data. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important. Statistical or traditional machine learning methods are inefficient, and the accuracy needs improvement. The methods based on deep learning can not directly process non-Euclidean spatial data, such as cell diagrams. In this study, we have developed multi-modal graph autoencoders and graph attention networks for scRNA-seq analysis, named scGAEGAT, based on graph neural networks. Cosine similarity, median L1 distance, and root-mean-squared error are used to measure the gene imputation performance of different methods with scGAEGAT. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score are used to measure the cell clustering performance of different methods with scGAEGAT. Experiment results show that the scGAEGAT model achieves promising performance in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels.