Interpretable integration of multiple spatial transcriptomics datasets with INSPIRE

Music City Center 

Recent advances in spatial transcriptomics technologies have led to diverse datasets, offering opportunities to explore tissue organizations within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. We present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We showcase INSPIRE's capabilities by applying it to diverse datasets. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development.

Spatial transcriptomics

Data integration

Deep learning

Data interpretation 

Section on Statistics in Genomics and Genetics