Tongxin Wang

This paper introduces SPCS, a two-factor smoothing method for spatial transcriptomics data that improves separability and biological interpretability compared to one-factor methods.

DEGAS transfers disease information from patients to cells using deep transfer learning, identifying PHF19high myeloma cells linked to progression.

MORONET is a multi-omics integration method using graph convolutional networks for biomedical classification, outperforming existing approaches and identifying disease-related biomarkers.

TSUNAMI is a tool for mining gene co-expression networks and performing downstream analysis, offering features like web interface, database access, and visualization.

BERMUDA corrects batch effects in scRNA-seq data and reveals hidden cellular subtypes by transferring information across batches.

LAmbDA reduces batch effects and improves subtype detection by integrating datasets with ambiguous labels across species and studies.