scVGAE: A ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation

Single-cell RNA sequencing (scRNA-seq) provides high-resolution measurements of cellular heterogeneity, but sparsity and technical zeros can obscure biological structure and complicate downstream analysis. We present scVGAE, a variational graph autoencoder for scRNA-seq imputation that integrates cell-cell graph propagation, a zero-inflated negative binomial (ZINB) likelihood, and direct expression reconstruction. scVGAE constructs a scalable cell graph using principal component analysis (PCA) followed by k-nearest neighbors, and encodes each cell into the parameters of a Gaussian latent distribution using graph convolutional networks (GCNs). A low-dimensional latent representation is obtained through stochastic reparameterization and is decoded both into gene-wise ZINB parameters and into a reconstructed expression matrix. Training jointly optimizes ZINB negative log-likelihood, mean-squared reconstruction error, and Kullback--Leibler divergence regularization. We evaluate scVGAE on 14 real-world scRNA-seq datasets against the original expression data and five established imputation methods: MAGIC, ALRA, DeepImpute, DCA, and GNNImpute. scVGAE achieves the highest mean Adjusted Rand Index (ARI) of 0.4681 and the second-highest mean Adjusted Mutual Information (AMI) of 0.5729 across the 14 datasets. These results demonstrate that a compact variational graph representation can preserve cell-class structure competitively across heterogeneous datasets while simultaneously producing an imputed expression matrix.