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Integrative Spatial Single-cell Analysis with Graph-based Feature Learning

View ORCID ProfileJunjie Zhu, View ORCID ProfileChiara Sabatti
doi: https://doi.org/10.1101/2020.08.12.248971
Junjie Zhu
1Department of Electrical Engineering, Stanford University
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  • For correspondence: junjie.zhu.jason@gmail.com
Chiara Sabatti
2Department of Statistics, Stanford University
3Department of Biomedical Data Science, Stanford University
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Abstract

We propose GLISS, a strategy to discover spatially-varying genes by integrating two data sources: (1) spatial gene expression data such as image-based fluorescence in situ hybridization techniques, and (2) dissociated whole-transcriptome single-cell RNA-sequencing (scRNA-seq) data. GLISS utilizes a graph-based association measure to select and link genes that are spatially-dependent in both data sources. GLISS can discover new spatial genes and recover cell locations in scRNA-seq data from landmark genes determined from SGE data. GLISS also offers a new dimension reduction technique to cluster the genes, while accounting for the inferred spatial structure of the cells. We demonstrate the utility of GLISS on simulated and real datasets, including datasets on the mouse olfactory bulb and breast cancer biopsies, and two spatial studies of the mammalian liver and intestine.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 13, 2020.
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Integrative Spatial Single-cell Analysis with Graph-based Feature Learning
Junjie Zhu, Chiara Sabatti
bioRxiv 2020.08.12.248971; doi: https://doi.org/10.1101/2020.08.12.248971
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Integrative Spatial Single-cell Analysis with Graph-based Feature Learning
Junjie Zhu, Chiara Sabatti
bioRxiv 2020.08.12.248971; doi: https://doi.org/10.1101/2020.08.12.248971

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