<p>The analysis of single-cell RNA sequencing (scRNA-seq) data is beset by formidable hurdles, including large feature space, widespread sparsity, noise contamination, and inter-batch variability, which collectively compromise the accuracy of cell clustering and subsequent downstream analyses. To overcome these obstacles, we present scCMA, a novel computational framework that synergistically combines a discriminative representation learning scheme with a masked reconstruction autoencoder architecture to generate stable and biologically meaningful cell embeddings. The contrastive module sharpens the distinction between cell types by maximizing similarities within types while minimizing them across types, thereby implicitly mitigating batch effects without requiring prior dataset information. Concurrently, the masked autoencoder learns to reconstruct randomly masked gene expression profiles, enabling the model to capture global transcriptional dependencies and identify rare biological features while diminishing the influence of noise and sparsity. Comprehensive evaluations on a diverse array of public datasets reveal that scCMA demonstrates superior performance in improved clustering precision, effectively corrects for batch differences without sacrificing biological variance, and exhibits remarkable proficiency in recognizing rare cellular subsets. Moreover, the embeddings generated by scCMA accurately reflect the temporal progression of cell development, facilitating the faithful modeling of cellular lineage progression.</p> Graphical abstract <p></p>