Linhua Wang

This study identifies an early neutrophil interferon program that predicts resistance to anti-PD1 therapy and limits durable anti-tumor immunity.

SEAGAL is a Python package that detects and visualizes spatial gene correlations in spatial transcriptomics data using the L-index method.

scGREAT is a tool for analyzing regulatory elements in single-cell multi-omics data, enabling heterogeneity analysis and regulatory state inference.

This paper introduces MinNet, a Siamese network with graph-based contrastive loss for integrating single-cell multi-omics data, showing its effectiveness in removing batch effects and improving downstream analysis.

This paper introduces ReSort, a method to improve cell type deconvolution in spatial transcriptomics by reducing batch effects, and shows it enhances accuracy in mouse breast cancer data.

The paper identifies a latent factor in brain RNA-seq data that reflects cell type and clinical heterogeneity, improving the identification of biological pathways and biomarkers.

The paper introduces Ensemble Integration (EI), a late integration method for multimodal biomedical data, achieving more accurate predictions than individual modalities and early integration approaches.

The paper introduces MIST, a tool that detects tissue regions and imputes missing gene-expression values in spatial transcriptomics data, improving accuracy and enabling downstream analysis.