Xiao Tan

SpaMTP integrates spatial metabolomics and transcriptomics data through a comprehensive analysis pipeline and visualization tools.

This study develops a spatial pharmaco-multiomics framework to map drug distribution, metabolic niches, and resistance mechanisms in medulloblastoma using integrated spatial and transcriptomic data.

STimage predicts gene expression and classifies cell types from histopathological images, improving robustness and interpretability for cancer and disease analysis.

The paper integrates 12 spatial and single-cell technologies to analyze cellular interactions and signatures in three skin cancers, identifying key therapeutic targets and genetic associations.

This paper benchmarks 20 methods for mapping trait-cell type associations by integrating GWAS and scRNA-seq data, finding CATCH to be the most robust approach.

This study shows that CD8+ T cells prevent reactivation of latent Mtb infection after immunosuppression, while B cells are not required for this protection.

This study uses spatial transcriptomics to analyze a DIPG-infiltrated brainstem, identifying invasion markers and ligand-receptor pairs involved in tumour-TME crosstalk.

This paper evaluates deep learning methods for predicting spatial gene expression from histology images in breast cancer, comparing their generalization performance across datasets.

This paper develops a spatial multi-omics platform to analyze immune responses in SARS-CoV-2-infected lung tissues, revealing macrophage infiltration and spatial heterogeneity in cytokine and interferon signaling.

This paper introduces HEMnet, a deep learning model that transfers molecular labels from immunohistochemistry to H&E images to identify cancer cells without manual annotation.

This study uses spatial transcriptomics to analyze human and mouse kidney tissues, identifying gene expression patterns, nephron segments, cell interactions, and genetic variants while preserving spatial context.

SpaCell integrates spatial gene expression and tissue morphology to improve cell type identification and image prediction.