Limin Li

SORT identifies multilevel gene programs and their spatial activities across diverse spatial transcriptomic datasets, revealing both shared and sample-specific patterns.

This paper introduces st-Xprop, a graph learning method for spatial domain detection in multi-modal spatial transcriptomics, improving clustering accuracy and biological relevance.

BiCLUM integrates unpaired single-cell multi-omics data by aligning cell and feature spaces through bilateral contrastive learning, outperforming existing methods in visualization and quantitative metrics.

CoupleVAE predicts post-perturbation single-cell RNA-seq data by using coupled variational autoencoders, achieving better accuracy than existing models in three real datasets.

This paper introduces XVGAE, a cross-view graph neural network that integrates gene expression, spatial coordinates, and histological images to identify spatial domains, achieving better clustering accuracy and biologically meaningful results.

WMW-A improves test power for small sample two-sample tests by using an auxiliary sample to construct a three-sample rank-based statistic.

XGSEA predicts gene set enrichment significance in one species using gene expression from another species through domain adaptation and regression.