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SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor

View ORCID ProfileDario Righelli, View ORCID ProfileLukas M. Weber, View ORCID ProfileHelena L. Crowell, View ORCID ProfileBrenda Pardo, View ORCID ProfileLeonardo Collado-Torres, View ORCID ProfileShila Ghazanfar, View ORCID ProfileAaron T. L. Lun, View ORCID ProfileStephanie C. Hicks, View ORCID ProfileDavide Risso
doi: https://doi.org/10.1101/2021.01.27.428431
Dario Righelli
1Department of Statistical Sciences, University of Padova, Padova, Italy
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Lukas M. Weber
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Helena L. Crowell
3Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
4SIB Swiss Institute of Bioinformatics, Zurich, Switzerland
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Brenda Pardo
5Escuela Nacional de Estudios Superiores Unidad Juriquilla, Universidad Nacional Autónoma de México, Queretaro, Mexico
6Lieber Institute for Brain Development, Baltimore, MD, USA
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Leonardo Collado-Torres
6Lieber Institute for Brain Development, Baltimore, MD, USA
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Shila Ghazanfar
7Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
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Aaron T. L. Lun
8Genentech, South San Francisco, CA, USA
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Stephanie C. Hicks
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Davide Risso
1Department of Statistical Sciences, University of Padova, Padova, Italy
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  • For correspondence: risso.davide@gmail.com
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Abstract

Motivation Spatially resolved transcriptomics is a new set of technologies to measure gene expression for up to thousands of genes at near-single-cell, single-cell, or sub-cellular resolution, together with the spatial positions of the measurements. Analyzing combined molecular and spatial information has generated new insights about biological processes that manifest in a spatial manner within tissues. However, to efficiently analyze these data, specialized data infrastructure is required, which facilitates storage, retrieval, subsetting, and interfacing with downstream tools.

Results Here, we describe SpatialExperiment, a new data infrastructure for storing and accessing spatially resolved transcriptomics data, implemented within the Bioconductor framework in the R programming language. SpatialExperiment extends the existing SingleCellExperiment for single-cell data from the Bioconductor framework, which brings with it advantages of modularity, interoperability, standardized operations, and comprehensive documentation. We demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms. SpatialExperiment is extendable to alternative technological platforms measuring expression and to new types of data modalities, such as spatial immunofluorescence or proteomics, in the future. We also provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData, and ggspavis packages.

Availability and Implementation SpatialExperiment is freely available from Bioconductor at https://bioconductor.org/packages/SpatialExperiment. The STexampleData, TENxVisiumData, and ggspavis packages are available from GitHub and will be submitted to Bioconductor.

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 4.0 International license.
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Posted January 27, 2021.
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SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor
Dario Righelli, Lukas M. Weber, Helena L. Crowell, Brenda Pardo, Leonardo Collado-Torres, Shila Ghazanfar, Aaron T. L. Lun, Stephanie C. Hicks, Davide Risso
bioRxiv 2021.01.27.428431; doi: https://doi.org/10.1101/2021.01.27.428431
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SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor
Dario Righelli, Lukas M. Weber, Helena L. Crowell, Brenda Pardo, Leonardo Collado-Torres, Shila Ghazanfar, Aaron T. L. Lun, Stephanie C. Hicks, Davide Risso
bioRxiv 2021.01.27.428431; doi: https://doi.org/10.1101/2021.01.27.428431

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