Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks

Abstract

With the development of high-throughput sequencing technology, the scale of single-cell RNA sequencing (scRNA-seq) data has surged. Its data are typically high-dimensional, with high dropout noise and high sparsity. Therefore, gene imputation and cell clustering analysis of scRNA-seq data is increasingly important. Statistical or traditional machine learning methods are inefficient, and improved accuracy is needed. The methods based on deep learning cannot directly process non-Euclidean spatial data, such as cell diagrams. In this study, we developed scGAEGAT, a multi-modal model with graph autoencoders and graph attention networks for scRNA-seq analysis based on graph neural networks. Cosine similarity, median L1 distance, and root-mean-squared error were used to measure the gene imputation performance of different methods for comparison with scGAEGAT. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score were used to measure the cell clustering performance of different methods for comparison with scGAEGAT. Experimental results demonstrated promising performance of the scGAEGAT model in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels.

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

“…The emergence of deep learning has revolutionized the inference of gene regulatory networks (GRNs) from single-cell RNA-sequencing (scRNA-seq) data, underscoring the utility of transformative machine learning architectures such as the attention mechanism and transformers. Prominent studies, including Lin and Ou-Yang [124], Xu et al [125], Feng et al [126], Ullah and Ben-Hur [127], and Xie et al [128], have utilized these architectures to devise models for GRN inference, highlighting their superior performance compared to conventional methodologies.…”

“…The method leveraged the gene expression motif technique to convert gene pairs into contiguous sub-vectors, which then served as the input for the transformer encoder. Furthermore, Feng et al [126] introduced scGAEGAT, a multi-modal model integrating graph autoencoders and graph attention networks for single-cell RNA-seq analysis, exhibiting a promising performance in gene imputation and cell clustering prediction.…”