Yuxi Liu

GatorPrism integrates spatial multi-omics data by balancing cross-omics consensus with modality-specific signals, achieving strong performance across clustering metrics and revealing tissue structure organization.

GatorDuo improves spatial domain identification in spatial transcriptomics by refining dual graphs with pseudo-label agreement and adaptive clustering.

GatorSC improves single-cell transcriptomic analysis by integrating multi-scale cell and gene graphs with a Mixture-of-Experts fusion framework, outperforming existing methods in clustering, imputation, and annotation.

GatorST improves spatial transcriptomic analysis by integrating graph-based modeling, pseudo-labeling, and contrastive meta-learning to enhance spatial domain identification and downstream tasks.

This paper introduces scSimGCL, a graph contrastive learning framework for scRNA-seq data that improves cell clustering through self-supervised pretraining and high-quality representation learning.