@article {68708,
	title = {Predictive and robust gene selection for spatial transcriptomics.},
	journal = {Nature Communications},
	volume = {14},
	year = {2023},
	month = {2023 Apr 12},
	pages = {2091},
	abstract = {<p>A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell{\textquoteright}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{\textquoteright}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.</p>
},
	keywords = {Gene Expression Profiling, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome},
	issn = {2041-1723},
	doi = {10.1038/s41467-023-37392-1},
	author = {Covert, Ian and Gala, Rohan and Wang, Tim and Svoboda, Karel and S{\"u}mb{\"u}l, Uygar and Lee, Su-In}
}
