Method speeds up mapping tissue regions from gene and image data
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification
Computer Vision and Pattern RecognitionArtificial IntelligenceMachine Learning
Summary
Understanding how different parts of tissue are organized can help scientists learn about health and disease. The authors created a new method called MP-MNCA that uses both gene data and tissue images to find these areas faster and just as accurately as older methods. This method focuses the analysis on important neighbors in the tissue, guided by how similar the tissue looks under a microscope. As a result, it reaches good results with less training time and often better accuracy.
What this means in practice
- •For bioinformatics teams: Generate tissue spatial domain maps more quickly using combined gene and image data for improved research workflows.
- •For pharmaceutical research labs: Accelerate analysis of tissue organization to better understand disease mechanisms and treatment targets.
Authors
Sidharthan Sivacoumar Chitra, Sanjeev Manivannan
Abstract
Spatial transcriptomics jointly captures molecular state, tissue morphology, and spatial organization, yet spatial-domain methods are typically compared only at late training checkpoints, obscuring differences in convergence efficiency. We introduce MP-MNCA, a morphology-prior masked neighbor cross-attention framework that preserves the full 3,000 dimensional highly-variable-gene representation while selectively routing molecular information across spatial neighbors, with histological similarity incorporated as a continuous attention prior under a self-supervised masked neighbor-contrastive objective. Across 12 heterogeneous human dorsolateral prefrontal cortex (DLPFC) sections, MP-MNCA is evaluated against established spatial-domain baselines using both convergence trajectories and epoch-wise spatial-domain visualizations. Our results show that MP-MNCA (i) reaches coherent spatial-domain structure in substantially fewer training epochs, as reflected quantitatively by convergence curves and qualitatively by progressive spatial grids, and (ii) maintains strong clustering quality, achieving a mean peak refined ARI of 0.546 versus 0.549 for the strongest baseline (STAIG), while attaining the highest mean refined NMI of 0.657 versus 0.640. Moreover, MP-MNCA outperforms STAIG on 7 of 12 sections in ARI and 11 of 12 in NMI, demonstrating faster convergence without sacrificing spatial-domain quality.