Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck

Our results show that combining masking-based perturbation with information bottleneck learning provides an effective strategy for extracting informative representations from noisy single-cell transcriptomic data. The proposed framework offers a robust solution for scRNA-seq clustering and may facilitate more reliable identification of cellular heterogeneity in complex biological systems.