Ke Xu

stDyer-image improves clustering of spatial transcriptomics and proteomics data by integrating morphological images directly into the clustering process.

TRAFICA improves TF-DNA binding affinity prediction by integrating open chromatin features and in vitro binding profiles, outperforming existing tools in both in vitro and in vivo settings.

HarmoDecon mitigates multi-scale biases in spatial cell-type deconvolution by using a semi-supervised deep learning model, improving accuracy in spatial domain clustering and marker gene correlations.

The paper introduces SpaMV, a method for integrating spatial multi-omics data that preserves modality-specific information and improves clustering and dimensionality reduction.

stDyer improves spatial domain clustering in SRT data by combining GMVAE and GATs with dynamic graphs and mini-batch sampling, outperforming existing methods.

IGSimpute improves scRNA-seq data imputation with high accuracy and interpretability, outperforming existing methods on multiple tissues.