Background <p>Single-cell RNA sequencing (scRNA-seq) enables the identification of cell types within complex biological systems, yet accurately classifying both known and novel cell types remains a significant challenge. Supervised learning methods perform well when all cell types are labeled in the training data, but struggle with unseen cell types, while rejection-based approaches can mitigate misclassification but fail to leverage unlabeled data for learning. Deep learning-based methods, such as MARS, offer promising solutions but often suffer from poor generalization to novel cell populations.</p> Results <p>We propose Semi-supervised learning for Robust Novel Cell-type identification (SRNC), a novel semi-supervised framework that enhances classification accuracy while effectively identifying unknown cell types. By integrating self-supervised feature learning with semi-supervised classification, SRNC leverages both labeled and unlabeled data to improve generalization. Evaluated across six benchmark scRNA-seq datasets, SRNC consistently outperforms state-of-the-art methods, achieving higher ARI, F1-score, and precision than both the rejection-based approach and deep-learning-based MARS. Moreover, SRNC demonstrates robustness across datasets from different laboratories and excels in imbalanced classification scenarios, accurately identifying rare cell populations that other methods often misclassify.</p> Conclusions <p>Our results demonstrate that SRNC is a powerful and adaptable tool for cell-type classification in scRNA-seq analysis. By leveraging semi-supervised learning, SRNC effectively identifies both known and novel cell types, surpassing competing methods in multiple performance metrics. Its ability to generalize across datasets and handle class imbalances makes it a valuable approach for discovering new cell types, advancing precision medicine, and improving our understanding of cellular heterogeneity.</p>