发表机构
University of Kentucky（肯塔基大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OmicSync是结合证据约束LLM推理的可靠性感知空间多组学聚类框架，集成KAN-GCN骨干等功能，在多组学基准测试中表现优异，OmicSync-R进一步提升了人乳腺癌的聚类性能。
AI 中文摘要
空间多组学技术可在每个组织位点联合分析基因表达、表面蛋白与组织学信息，但多数空间域发现方法仅提供聚类分配结果，未说明分配可靠性、模态贡献或域决策的可信度。本文提出OmicSync，一种可靠性感知空间多组学框架，将无监督域聚类与证据约束大语言模型（LLM）推理结合，使用模型生成的每位点信号，包括分配置信度、认知路由不确定性及模态路由权重。这些信号被转换为结构化证据词典，用于生成标准、分步、反事实、对比及聚焦不确定性的解释。OmicSync集成KAN-GCN骨干网络，包含空间编码、跨模态融合、不确定性感知路由、细胞类型监督及缺失模态插补功能。我们进一步推出OmicSync-R，通过将自动计算的推理质量得分作为REINFORCE奖励，闭合推理-聚类循环，使推理连贯性能在不通过语言模型反向传播的情况下塑造潜在结构。在四个10x CytAssist FFPE空间蛋白质组学基准测试中，OmicSync在人扁桃体（1.44）、胶质母细胞瘤（1.78）及扁桃体附加样本（1.22）上取得最佳平均排名，在人乳腺癌（2.33）上排名第二；OmicSync-R进一步将人乳腺癌的调整兰德指数（ARI）从45.73提升至46.72，且在9项聚类指标中的6项上优于现有方法。综上，OmicSync与OmicSync-R可实现由证据约束推理引导的、可靠性感知的、位点级可审计的空间域发现。
英文摘要
Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discovery methods provide only cluster assignments, without indicating assignment reliability, modality contributions, or why a domain decision should be trusted. We present OmicSync, a reliability-aware spatial multi-omics framework that couples unsupervised domain clustering with evidence-constrained LLM reasoning using model-derived per-spot signals, including assignment confidence, epistemic routing uncertainty, and modality-routing weights. These signals are converted into structured evidence dictionaries and used to generate standard, stepwise, counterfactual, contrastive, and uncertainty-focused explanations. OmicSync integrates a KAN-GCN backbone with spatial encoding, cross-modal fusion, uncertainty-aware routing, cell-type supervision, and missing-modality imputation. We further introduce OmicSync-R, which closes the reasoning-clustering loop by using automatically computed reasoning-quality scores as REINFORCE rewards, allowing reasoning coherence to shape the latent structure without backpropagating through the language model. Across four 10x CytAssist FFPE spatial proteomics benchmarks, OmicSync achieves the best average rank on Human Tonsil (1.44), Glioblastoma (1.78), and Tonsil Add-on (1.22), and second-best on Human Breast Cancer (2.33). OmicSync-R further improves ARI on Human Breast Cancer from 45.73 to 46.72 and outperforms existing methods on six of nine clustering metrics. Together, OmicSync and OmicSync-R enable reliability-aware, spot-level auditable spatial domain discovery guided by evidence-constrained reasoning.
