Zixiang Pan

MeHi-SCC improves single-cell RNA-seq clustering by integrating meta-learning and multi-dataset information to identify cell subtypes in large-scale atlases.

This paper introduces ConGI, a method that integrates gene expression and histopathological images via contrastive learning to improve spatial domain identification in spatial transcriptomics.

ADClust is a parameter-free deep embedding clustering method for scRNA-seq data that automatically determines the number of clusters and outperforms existing methods.

This paper introduces GraphCS, a scalable GNN method for single-cell classification, showing improved performance on large datasets.