SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells
Published in Bioinformatics, 2020
Recommended citation: Xiao Tan, Andrew Su (2020). "SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells" Bioinformatics, Vol 36, . (2293–2294). https://doi.org/10.1093/bioinformatics/btz914
Motivation Spatial transcriptomics (ST) 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 ST data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology.
Results We developed a 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 data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy.
Authors: Xiao Tan, Andrew Su, Minh Tran, Quan Nguyen