Combining single-cell data with maps of tissue structure can be slow and tedious. In addition, machine learning algorithms may be limited by the data used to train them. To overcome these challenges, Weill Cornell Medicine researchers have developed a computational method to map the architecture of human tissues. The researchers say their approach promises to accelerate studies on organ-scale cellular interactions and could enable powerful new diagnostic strategies for a wide range of diseases.
Their findings are published in Nature Methods, in a paper titled, “Unsupervised discovery of tissue architecture in multiplexed imaging.”
