AllenDigger, a Tool for Spatial Expression Data Visualization, Spatial Heterogeneity Delineation, and Single-Cell...

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• Spatial transcriptomics offers insights into cellular organization in development, cancer, and neuroscience.
• Technical challenges and immature analysis methods limit the broad application of spatial transcriptomics.
• The Allen Brain Atlas (ABA) provides valuable spatial gene expression data for the mouse brain.

Purpose of the Study:

• To develop a user-friendly toolkit for accessing and analyzing Allen Brain Atlas (ABA) data.
• To enable precise visualization of spatial gene distribution and brain heterogeneity.
• To facilitate the accurate registration of single-cell transcriptomics (scRNA-seq) data to anatomical regions.

Main Methods:

• Data collection and preprocessing from the Allen Brain Atlas (ABA).
• Development of a query system for visualizing spatial gene expression.
• Application of machine learning for accurate cell registration to fine anatomical regions.

Main Results:

• A toolkit, AllenDigger, was created for efficient spatial gene expression analysis.
• The toolkit allows for detailed visualization of gene distribution and brain spatial heterogeneity.
• High-accuracy registration of scRNA-seq data to specific brain regions was achieved.

Conclusions:

• AllenDigger provides a cost-effective solution for precise spatial gene expression queries.
• The toolkit enhances the interpretation of scRNA-seq data by adding valuable spatial context.
• This resource will significantly benefit the neuroscience and genomics research community.