A clustering method for single-cell RNA sequencing data based on denoising and masking learning

By effectively combining probabilistic denoising with masking-based representation learning, scDMAC provides a powerful solution for addressing dropout and sparsity issues in scRNA-seq data. The improved clustering performance suggests that integrating distribution-aware denoising with feature reconstruction enhances the extraction of biologically meaningful representations, making scDMAC a promising tool for single-cell transcriptomic analysis.