TY - JOUR T1 - Annotation of Spatially Resolved Single-cell Data with STELLAR JF - bioRxiv DO - 10.1101/2021.11.24.469947 SP - 2021.11.24.469947 AU - Maria Brbić AU - Kaidi Cao AU - John W. Hickey AU - Yuqi Tan AU - Michael P. Snyder AU - Garry P. Nolan AU - Jure Leskovec Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/11/25/2021.11.24.469947.abstract N2 - Spatial protein and RNA imaging technologies have been gaining rapid attention but current computational methods for annotating cells are based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method that utilizes spatial and molecular cell information to automatically assign cell types from an annotated reference set as well as discover new cell types and cell states. STELLAR transfers annotations across different dissection regions, tissues, and donors and detects higher-order tissue structures with dramatic time savings.Competing Interest StatementThe authors have declared no competing interest. ER -