Recently, Dr. Zuo Chunman from the DHU Institute of Artificial Intelligence has made vital progress in elucidating tumor heterogeneity from spatially resolved transcriptomics data. The result entitled Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning was published in Nature Communications, an internationally distinguished journal. Zuo Chunman is the first and co-corresponding author whose affiliation is the Institute of Artificial Intelligence, Donghua University.
Fig.1 Overview of the stMVC model
Dr. Zuo Chunman, in collaboration with Researcher Chen Luonan from the Center for Excellence in Molecular Cell Science, CAS, has introduced stMVC (Spatial Transcriptomics data analysis by Multiple View Collaborative-learning). It’s a multi-view graph collaborative-learning model that integrates histological image data, spatial location, gene expression, and tumor position to elucidate tumor heterogeneity from spatially resolved transcriptomics data. With the promotion of spatial transcriptomics, stMVC will boost the clinical and prognostic applications of spatial transcriptomics data.
Paper link: https://doi.org/10.1038/s41467-022-33619-9
