CellPhenoX: An Explainable Machine Learning Method for Identifying Cell Phenotypes To Predict Clinical Outcomes from Single‐Cell Multi‐Omics

Single‐cell technologies have transformed the understanding of disease heterogeneity, but linking cell‐level phenotypic alterations to clinical outcomes becomes increasingly challenging as single‐cell datasets continue to expand. This is further complicated by the lack of interpretability in existing methods and the difficulty of detecting interaction effects—nonlinear dependencies between factors like sex, age, and disease. To address this, a novel explainable machine learning method, CellPhenoX, is developed to identify cell‐specific phenotypes and interaction effects linked to clinical outcomes. CellPhenoX integrates classification models, explainable artificial intelligence (AI) techniques, and a statistical framework to generate interpretable, cell‐specific scores to uncover condition‐associated cell populations. Extensive benchmarking and applications demonstrate the efficacy of CellPhenoX across diverse single‐cell study designs, including the dedicated and disease‐motivated simulations, binary disease‐control comparisons, and severity‐stratified patient cohorts. Notably, CellPhenoX identifies an activated monocyte phenotype in COVID‐19, with expansion correlated with disease severity after adjusting for covariates and interactive effects. It also uncovers a fibroblast‐specific state transition gradient predicting tissue inflammation in chronic diseases, and identifies therapy‐induced T cell changes and biomarkers linked to the tumor microenvironment. By integrating interpretability into clinical classification, CellPhenoX offers a powerful framework for translating single‐cell findings into clinical impact.