Learning phenotype associated signature in spatial transcriptomics with PASSAGE

Abstract Spatially resolved transcriptomics (SRT) is poised to advance our understanding of cellular organization within complex tissues under various physiological and pathological conditions at unprecedented resolution. Despite the development of numerous computational tools that facilitate the automatic identification of statistically significant intra-/inter-slice patterns (like spatial domains), these methods typically operate in an unsupervised manner, without leveraging sample characteristics like physiological/pathological states. Here we present PASSAGE ( P henotype A ssociated S patial S ignature A nalysis with G raph-based E mbedding), a rationally-designed deep learning framework for characterizing phenotype-associated signatures across multiple heterogeneous spatial slices effectively. In addition to its outstanding performance in systematic benchmarks, we have demonstrated PASSAGE’s unique capability in identifying sophisticated signatures in multiple real-world datasets. The full package of PASSAGE is available at https://github.com/gao-lab/PASSAGE .