scSemiPLC: a semi-supervised learning framework for annotating single-cell RNA-Seq data by generating pseudo-labels through clustering

This work proposes a novel cell annotation training framework, scSemiPLC, which significantly enhances the efficiency and accuracy of annotation by fully leveraging unlabeled data. In the semi-supervised learning component, the framework innovatively generates pseudo-labels through clustering. Subsequently, it evaluates the reliability of these pseudo-labels and assigns corresponding weights, thereby balancing both their quantity and quality. This approach provides new insights into the direction of automatic cell annotation within the realm of semi-supervised learning.