SpaGE: Spatial Gene Enhancement using scRNA-seq
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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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Tangram outperformed other imputation methods such as gimVI and SpaGE with respect to various accuracy metrics and scalability.
“It was shown to outperform other imputation methods such as gimVI225 and SpaGE226 with respect to various accuracy metrics and scalability215.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [226]
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- Authors
- Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T.
- Affiliations
- Leiden University Medical Center (NL)
- The Netherlands Cancer Institute (NL)
- Corresponding author
- Reinders, Marcel J T
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-09-21
- Volume
- 48
- Issue
- 18
- Pages
- e107-e107
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC7544237
- OpenAlex
- W3087138957
- Cited by
- 207
- References
- 29
- Citations by year
- 2026: 27 · 2025: 44 · 2024: 55 · 2023: 33 · 2022: 30 · 2021: 17 · 2020: 1
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc)
- Leiden Repository (Leiden University) (submittedVersion, cc-by-nc)
- Research Repository (Delft University of Technology) (submittedVersion, cc-by-nc)
- Funders
- European Commission (675743)
- H2020 (861190)
- NWO TTW (17126)
- ZonMw (40-00812-98-16012)
- PPP allowance
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- spatial transcriptomics, single-cell RNA sequencing, spatial gene expression prediction, Allen Mouse Brain Atlas
- MeSH terms
- RNA-Seq, Animals, Software, Databases, Genetic, Mice, Single-Cell Analysis, Transcriptome, Datasets as Topic
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