提出可评估可靠性并生成解释的多组学空间聚类方法
OmicSync: Reliability-Aware Spatial Multi-Omics Clustering with Evidence-Constrained LLM Reasoning
- 用置信度、不确定性等信号构建证据库，驱动大模型生成多类解释
- 在4个组织样本上平均排名领先，乳腺癌聚类精度提升至46.72 ARI
- 适合需要可解释性与可信度评估的生物医学空间组学研究
空间多组学技术在组织切片中联合检测基因表达、表面蛋白和组织形态，但现有方法仅提供聚类结果，缺乏可靠性评估、模态贡献分析及决策可信依据。本文提出OmicSync，一种可靠性感知的空间多组学框架，将无监督域聚类与基于模型生成的位点级信号（包括置信度、认知不确定性、模态路由权重）约束的大语言模型推理结合。这些信号转化为结构化证据字典，生成标准、分步、反事实、对比和不确定性聚焦的解释。OmicSync采用KAN-GCN主干网络，集成空间编码、跨模态融合、不确定性感知路由、细胞类型监督与缺失模态补全。进一步提出OmicSync-R，通过自动计算的推理质量评分作为REINFORCE奖励，闭环优化推理一致性，不反向传播至语言模型。在四个10x CytAssist FFPE空间蛋白质组学基准上，OmicSync在扁桃体（1.44）、胶质母细胞瘤（1.78）和附加扁桃体（1.22）任务中平均排名最优，乳腺癌（2.33）第二；OmicSync-R将乳腺癌ARI从45.73提升至46.72，并在九项指标中的六项超越现有方法。OmicSync与OmicSync-R共同实现可审计、可靠性感知的位点级空间域发现。
原文摘要 · Abstract (English)
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.
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