ABSTRACT
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.
