# Spatial Multi-omic Data Integration

Spatial multi-omic data pose two jobs at once: merging molecular layers such as RNA and protein measured on the same tissue section, and transferring cell-type labels from a reference dataset onto a new sample. DIRAC treats both as domain adaptation, using a graph neural network and an adversarial discriminator to learn embeddings that are insensitive to batch and modality but discriminative for cell type. On simulated and real DBiT-seq data it beat comparison methods on domain recovery and mixing, and in glioblastoma tissue it identified a microvascular proliferation domain the other methods missed.

Spatial multi-omic data pose two jobs at once: merging molecular layers such as RNA and protein measured on the same tissue section, and transferring cell-type labels from a reference dataset onto a new sample. DIRAC treats both as domain adaptation, using a graph neural network and an adversarial discriminator to learn embeddings that are insensitive to batch and modality but discriminative for cell type. On simulated and real DBiT-seq data it beat comparison methods on domain recovery and mixing, and in glioblastoma tissue it identified a microvascular proliferation domain the other methods missed.