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Annotation of Spatially Resolved Single-cell Data with STELLAR

By Maria Brbic, Kaidi Cao, John W Hickey, Yuqi Tan, Michael Snyder, Garry Nolan, Jure Leskovec

Posted 25 Nov 2021
bioRxiv DOI: 10.1101/2021.11.24.469947

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.

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