AllenDigger, a Tool for Spatial Expression Data Visualization, Spatial Heterogeneity Delineation, and Single-Cell Registration Based on the Allen Brain Atlas

Spatial transcriptomics can be used to capture cellular spatial organization and has facilitated new insights into different biological contexts, including developmental biology, cancer, and neuroscience. However, its wide application is still hindered by its technical challenges and immature data analysis methods. Allen Brain Atlas (ABA) provides a great source for spatial gene expression throughout the mouse brain at various developmental stages with in situ hybridization image data. To the best of our knowledge, the portal developed to access spatial expression data is not very useful to biologists. Here, we developed a toolkit to collect and preprocess expression data from the ABA and allow a friendlier query to visualize the spatial distribution of genes of interest, characterize the spatial heterogeneity of the brain, and register cells from single-cell transcriptomics data to fine anatomical brain regions via machine learning methods with high accuracy. AllenDigger will be very helpful to the community in precise spatial gene expression queries and add extra spatial information to further interpret the scRNA-seq data in a cost-effective manner.

Automated summary

Spatial transcriptomics captures cellular spatial organization and provides new insights into various biological contexts. However, its wide application is hindered by technical challenges and immature data analysis methods. The Allen Brain Atlas (ABA) provides spatial gene expression data, but the current portal is not user-friendly. We developed AllenDigger, a toolkit that collects and preprocesses ABA expression data, visualizes spatial gene distribution, characterizes brain heterogeneity, and registers single-cell transcriptomics data to anatomical brain regions with high accuracy using machine learning methods.