Print Email Facebook Twitter SpaGE Title SpaGE: Spatial Gene Enhancement using scRNA-seq Author Abdelaal, T.R.M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Mourragui, S.M.C. (TU Delft Pattern Recognition and Bioinformatics; Netherlands Cancer Institute) Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2020 Abstract Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas. Subject OA-Fund TU Delft To reference this document use: http://resolver.tudelft.nl/uuid:6dfdae6f-65f6-4f45-9e9a-f7e3f507b185 DOI https://doi.org/10.1093/nar/gkaa740 ISSN 0305-1048 Source Nucleic acids research, 48 (18) Part of collection Institutional Repository Document type journal article Rights © 2020 T.R.M. Abdelaal, S.M.C. Mourragui, A.M.E.T.A. Mahfouz, M.J.T. Reinders Files PDF gkaa740.pdf 11.16 MB Close viewer /islandora/object/uuid:6dfdae6f-65f6-4f45-9e9a-f7e3f507b185/datastream/OBJ/view