RT Journal Article
SR Electronic
T1 Annotation of Spatially Resolved Single-cell Data with STELLAR
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.24.469947
DO 10.1101/2021.11.24.469947
A1 Maria Brbić
A1 Kaidi Cao
A1 John W. Hickey
A1 Yuqi Tan
A1 Michael P. Snyder
A1 Garry P. Nolan
A1 Jure Leskovec
YR 2021
UL http://biorxiv.org/content/early/2021/11/25/2021.11.24.469947.abstract
AB 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.