SpaGE
Spatial Gene Enhancement using scRNA-seq
Tamim R.M. Abdelaal (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
Soufiane Mourragui (TU Delft - Pattern Recognition and Bioinformatics, Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Ahmed Mahfouz (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)
MJT Reinders (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
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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.
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