scGANSL: Graph Attention Network with Subspace Learning for scRNA-seq Data Clustering

Abstract

Single-cell RNA sequencing (scRNA-seq) has become a crucial technology for analyzing cellular diversity at the single-cell level. Cell clustering is crucial in scRNA-seq data analysis as it accurately identifies distinct cell types and uncovers potential subpopulations. However, most existing scRNA-seq methods rely on a single view for analysis, leading to an incomplete interpretation of the scRNA-seq data. Furthermore, the high dimensionality of the scRNA-seq data and the inevitable noise pose significant challenges for clustering tasks. To address these challenges, in this study, we introduce a novel clustering method, called graph attention network with subspace learning (scGANSL), for scRNA-seq data clustering. Specifically, the proposed scGANSL method first constructs two views using highly variable genes (HVGs) screening and principal component analysis (PCA). They are then individually fed into a multiview shared graph autoencoder, where clustering labels guide the learning of latent representations and the coefficient matrix. Furthermore, the proposed method integrates a zero-inflated negative binomial (ZINB) model into a self-supervised graph attention autoencoder to learn latent representations more effectively. To preserve both local and global structures of scRNA-seq data in the latent representation space, we introduce a local learning and self-expression strategy to guide model training. Experimental results across various scRNA-seq data sets demonstrate that the proposed scGANSL model significantly outperforms other state-of-the-art scRNA-seq data clustering methods.