这篇文章介绍了 CellPhenoX，这是一种用于单细胞多组学数据的可解释机器学习方法，旨在预测临床表型。该方法通过整合分类模型和可解释人工智能（XAI）技术（如 SHAP 值），生成 可解释的细胞特异性分数，以识别与临床结果相关的细胞群体和相互作用效应。文章详细描述了CellPhenoX的方法论，包括如何使用邻域丰度矩阵（NAM）和降维技术来预测临床表型，同时纳入协变量和相互作用项。通过在模拟数据集、COVID-19单细胞蛋白质组学和溃疡性结肠炎纤维母细胞转录组学数据集上的应用，该研究展示了 CellPhenoX 在检测疾病相关细胞(包括稀有细胞类型和 性/年龄等相互作用效应)方面的卓越性能，并将其表现与现有方法（如 CNA 和 MiloR）进行了比较。References: Young J, Inamo J, Caterer Z, et al. CellPhenoX: An eXplainable Cell-specific machine learning method to predict clinical Phenotypes using single-cell multi-omics[J]. bioRxiv, 2025.
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