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MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial transcriptomics data in Bioconductor

Bárbara Zita Peters Couto, Nicholas Robertson, View ORCID ProfileEllis Patrick, Shila Ghazanfar
doi: https://doi.org/10.1101/2023.05.16.541040
Bárbara Zita Peters Couto
1School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
2Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
3Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
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Nicholas Robertson
1School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
2Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
3Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
42, Science Park, Hong Kong SAR, China
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Ellis Patrick
1School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
2Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
3Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
42, Science Park, Hong Kong SAR, China
5Centre for cancer research, The Westmead Institute for Medical Research, The University of Sydney, NSW 2145, Australia
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  • ORCID record for Ellis Patrick
  • For correspondence: shila.ghazanfar@sydney.edu.au ellis.patrick@sydney.edu.au
Shila Ghazanfar
1School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
2Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
3Sydney Precision Data Science Centre, University of Sydney, NSW 2006, Australia
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  • For correspondence: shila.ghazanfar@sydney.edu.au ellis.patrick@sydney.edu.au
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Abstract

Imaging-based spatial transcriptomics technologies have achieved subcellular resolution, enabling detection of individual molecules in their native tissue context. Data associated with these technologies promises unprecedented opportunity towards understanding cellular and subcellular biology. However, in R/Bioconductor there is a scarcity of existing computational infrastructure to represent such data, and particularly to summarise and transform it for existing widely adopted computational tools in single cell transcriptomics analysis, including SingleCellExperiment and SpatialExperiment classes. With the emergence of several commercial offerings of imaging-based spatial transcriptomics, there is a pressing need to develop consistent data structure standards for these technologies at the individual molecule level. To this end, we have developed MoleculeExperiment, an R/Bioconductor package, which i) stores molecule and cell segmentation boundary information at the molecule-level, ii) standardises this molecule-level information across different imaging-based ST technologies, including 10x Genomics’ Xenium, and iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. Overall, MoleculeExperiment is generally applicable as a data infrastructure class for consistent analysis of imaging-based spatial transcriptomics data.

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 4.0 International license.
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Posted May 18, 2023.
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MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial transcriptomics data in Bioconductor
Bárbara Zita Peters Couto, Nicholas Robertson, Ellis Patrick, Shila Ghazanfar
bioRxiv 2023.05.16.541040; doi: https://doi.org/10.1101/2023.05.16.541040
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MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial transcriptomics data in Bioconductor
Bárbara Zita Peters Couto, Nicholas Robertson, Ellis Patrick, Shila Ghazanfar
bioRxiv 2023.05.16.541040; doi: https://doi.org/10.1101/2023.05.16.541040

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