Joe Poh-Sheng Yeong

This study integrates spatial transcriptomics and single-cell data from gastric cancer samples to reveal tumor ecosystem dynamics, evolutionary trajectories, and immune microenvironment features.

This study compares a virtual staining AI model's performance on same-tissue versus serial-tissue sections using CD3+ T-cell ground truth, finding better performance with same-tissue data.

FAST improves spatial dimension reduction for multi-section SRT data by modeling count data and spatial smoothness, showing better performance and speed on real datasets.

PRECAST integrates spatial transcriptomics data from multiple sources, improving cell and domain detection through probabilistic embedding, clustering, and alignment.

SpatialDEG identifies differentially expressed genes in spatially resolved transcriptomic data by integrating spatial information, showing controlled type-I error rates in simulations and applied to human and mouse datasets.

This paper introduces DR-SC, a method that jointly performs dimension reduction and spatial clustering for single-cell and spatial transcriptomics data, improving accuracy and biological relevance.

SC-MEB is a spatial clustering method for transcriptomics data that outperforms existing methods in clustering performance and scalability.