MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial transcriptomics data
Author(s): Bárbara Zita Peters Couto,Ellis Patrick,Shila Ghazanfar
Affiliation(s): School of Mathematics and Statistics, University of Sydney
Social media: https://www.linkedin.com/in/b%C3%A1rbara-zita-peters-couto-bab4a1127/
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, there is a dearth of existing computational infrastructure to represent such data, and particularly to summarise and transform into 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’s Xenium, and iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. We motivate the use of the generally applicable MoleculeExperiment class as a data infrastructure for a consistent analysis of imaging-based spatial transcriptomics data. (See vignette at: https://sydneybiox.github.io/MoleculeExperiment/articles/MoleculeExperiment.html)