ELISA让单细胞数据生成可解释的生物学假设，打通基因表达与自然语言理解的桥梁。
ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
- 融合基因表达嵌入与语义检索，实现多模态查询响应
- 在6个数据集上显著优于基线方法，基因签名查询提升5.98倍效应量
- 适合生物学家探索单细胞数据，生成可验证的科学假说
将单细胞RNA测序（scRNA-seq）数据转化为机制性生物学假设仍是关键瓶颈，因智能体系统缺乏对转录组表示的直接访问，而表达基础模型对自然语言不透明。本文提出ELISA（Embedding-Linked Interactive Single-cell Agent），一个可解释框架，整合scGPT表达嵌入、基于BioBERT的语义检索与大语言模型（LLM）推理，支持交互式单细胞发现。自动查询分类器根据输入类型（基因特征、自然语言概念或混合）路由至不同处理流程。集成分析模块在嵌入数据上直接完成：60+通路活性评分、280+配体-受体互作预测、条件感知比较分析及细胞类型比例估计。在炎症性肺病、儿童与成人癌症、类器官、健康组织和神经发育等6个异质数据集上，ELISA显著优于CellWhisperer、BM25及随机基线（联合置换检验，$p < 2 imes10^{-5}$），尤其在基因签名查询中表现突出（MRR Cohen's $d = 5.98$）。ELISA复现已有文献发现（平均复合得分0.88），并通过基于证据的LLM推理生成候选假说，弥合转录组探索与生物学发现之间的鸿沟。
原文摘要 · Abstract (English)
Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies scGPT expression embeddings with BioBERT-based semantic retrieval and LLM-mediated interpretation for interactive single-cell discovery. An automatic query classifier routes inputs to gene marker scoring, semantic matching, or reciprocal rank fusion pipelines depending on whether the query is a gene signature, natural language concept, or mixture of both. Integrated analytical modules perform pathway activity scoringacross 60+ gene sets, ligand-receptor interaction prediction using 280+ curated pairs, condition-aware comparative analysis, and cell-type proportion estimation all operating directly on embedded data without access to the original count matrix. Benchmarked across six diverse scRNA-seq datasets spanning inflammatory lung disease, pediatric and adult cancers, organoid models, healthy tissue, and neurodevelopment, ELISA significantly outperforms CellWhisperer, a classical lexical retriever (BM25), and a random baseline in cell type retrieval (combined permutation test, $p < 2\times10^{-5}$ for each), with particularly large gains on gene-signature queries (Cohen's $d = 5.98$ for MRR). ELISA replicates published biological findings (mean composite score 0.88), and generates candidate hypotheses through grounded LLM reasoning, bridging the gap between transcriptomic data exploration and biological discovery.
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