Abstract
Despite significant advancements in single-cell representation learning, scalability and managing sparsity and dropout events continue to challenge the field as scRNA-seq datasets expand. While current computational tools struggle to maintain both efficiency and accuracy, the accurate connection of these dropout events to specific biological functions usually requires additional, complex experiments, often hampered by potential inaccuracies in cell-type annotation. To tackle these challenges, the Zero-Inflated Graph Attention Collaborative Learning (ZIGACL) method has been developed. This innovative approach combines a Zero-Inflated Negative Binomial model with a Graph Attention Network, leveraging mutual information from neighboring cells to enhance dimensionality reduction and apply dynamic adjustments to the learning process through a co-supervised deep graph clustering model. ZIGACL's integration of denoising and topological embedding significantly improves clustering accuracy and ensures similar cells are grouped closely in the latent space. Comparative analyses across nine real scRNA-seq datasets have shown that ZIGACL significantly enhances single-cell data analysis by offering superior clustering performance and improved stability in cell representations, effectively addressing scalability and managing sparsity and dropout events, thereby advancing our understanding of cellular heterogeneity.