SpatialCCA: Spatially aware method for cross-tissue integration in Spatial Transcriptomics

Thomas M. Menino Convention & Exhibition Center 

Spatial transcriptomics (ST) enables genome-wide expression profiling with preserved spatial context. However, integrating multiple tissue sections remains challenging due to the lack of direct spatial correspondence across different samples. We present SpatialCCA, a method for joint dimension reduction of two ST tissue sections that embeds them into a shared latent space while incorporating spatial information. SpatialCCA extends canonical correlation analysis through a kernel-based spatial regularization and models tissue-specific variation to mitigate batch effects. The resulting embedding captures both cross-section transcriptional similarity and within-tissue spatial organization. Using simulation generated by randomly partitioning real ST tissue data into two sets of spatial spots, we show that SpatialCCA yields embeddings with stronger correlation with the original coordinates compared to embeddings derived from gene profiles alone. By improving estimation of cross-tissue relationships, SpatialCCA provides a principled framework for integrating two ST slices and can serve as an effective pre-processing step for downstream analysis.

Spatial Transcriptomics

Canonical Correlation Analysis

Dimension Reduction

Cross-tissue Integration 

Section on Statistics in Genomics and Genetics