Srnc: semi-supervised learning for robust novel cell-type identification in single cell RNA sequencing data

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. 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. 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.