SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells

MotivationSpatial transcriptomics technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for spatial transcriptomics data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology.\n\nResultsWe developed an user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially-barcoded spots in a tissue. We show the integration approach outperforms the use of gene count alone or imaging data alone to create deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy.\n\nAvailabilityThe SpaCell package is open source under a MIT license and it is available at https://github.com/BiomedicalMachineLearning/SpaCell\n\nContactquan.nguyen@uq.edu.au