MP-MNCA：融合形态学先验的空间转录组域识别方法
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification
arXiv 上新出的一篇空间转录组方法论文，MP-MNCA 用形态学先验做注意力，收敛比 STAIG 快很多，做空间域识别的可以看看
MP-MNCA 是一种基于形态学先验掩码邻居交叉注意力机制的空间转录组分析框架，保留了完整的 3,000 维高变基因表示。该方法将组织切片的病理学相似度作为连续注意力先验，采用自监督掩码邻居对比目标进行训练。在 12 个人类背外侧前额叶皮层（DLPFC）切片上，MP-MNCA 达到峰值精炼 ARI 均值 0.546，与最强基线 STAIG 的 0.549 接近，NMI 均值 0.657 则高于后者的 0.640。该方法在 12 个切片中的 7 个 ARI 和 11 个 NMI 上超过 STAIG，且收敛所需训练轮数明显更少。
MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification
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.
