  Single-cell RNA sequencing (scRNA-seq) has revolutionized our ability to
study individual cellular distinctions and uncover unique cell characteristics.
However, a significant technical challenge in scRNA-seq analysis is the
occurrence of "dropout" events, where certain gene expressions cannot be
detected. This issue is particularly pronounced in genes with low or sparse
expression levels, impacting the precision and interpretability of the obtained
data. To address this challenge, various imputation methods have been
implemented to predict such missing values, aiming to enhance the analysis's
accuracy and usefulness. A prevailing hypothesis posits that scRNA-seq data
conforms to a zero-inflated negative binomial (ZINB) distribution.
Consequently, methods have been developed to model the data according to this
distribution. Recent trends in scRNA-seq analysis have seen the emergence of
deep learning approaches. Some techniques, such as the variational autoencoder,
incorporate the ZINB distribution as a model loss function. Graph-based methods
like Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) have
also gained attention as deep learning methodologies for scRNA-seq analysis.
This study introduces scVGAE, an innovative approach integrating GCN into a
variational autoencoder framework while utilizing a ZINB loss function. This
integration presents a promising avenue for effectively addressing dropout
events in scRNA-seq data, thereby enhancing the accuracy and reliability of
downstream analyses. scVGAE outperforms other methods in cell clustering, with
the best performance in 11 out of 14 datasets. Ablation study shows all
components of scVGAE are necessary. scVGAE is implemented in Python and
downloadable at https://github.com/inoue0426/scVGAE.
