Joe Poh-Sheng Yeong

Spatially Resolved Tumor Ecosystems and Cell States in Gastric Adenocarcinoma Progression and Evolution

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

FAST: a fast and scalable factor analysis for spatially aware dimension reduction of multi-section spatial transcriptomics 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.

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

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

SpatialDEG: Identification of differentially expressed genes by leveraging spatial information in spatially resolved transcriptomic studies

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.

Joint dimension reduction and clustering analysis for single-cell RNA-seq and spatial transcriptomics data

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: spatial clustering with hidden Markov random field using empirical Bayes

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