Predictive and robust gene selection for spatial transcriptomics

Ian Covert, Rohan Gala, Tim Wang, Karel Svoboda, Uygar Sümbül () and Su-In Lee ()
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Ian Covert: University of Washington
Rohan Gala: Allen Institute for Brain Science
Tim Wang: HHMI Janelia Research Campus
Karel Svoboda: HHMI Janelia Research Campus
Uygar Sümbül: Allen Institute for Brain Science
Su-In Lee: University of Washington

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell’s molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST’s binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

Date: 2023
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DOI: 10.1038/s41467-023-37392-1

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