Min Jian

StereoMM integrates spatial transcriptomic data and histological images to improve spatial domain recognition and tumor heterogeneity analysis in cancer tissues.

The paper develops VID, an ensemble machine learning model to predict virus-infected cells in scRNA-seq data, revealing biological differences between infected and uninfected cells in virus-induced cancers.

Stereo-CITE-seq combines CITE-seq with Stereo-seq to simultaneously profile whole transcriptomes and protein markers on the same fresh-frozen tissue section.

SpatialGlue integrates spatial multi-omics data using a graph neural network to improve spatial domain detection and cross-modality correlation analysis.