Nat Methods 2022 06 2;19(6):759-769. Epub 2022 Jun 2.
Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. Read More