用机器学习整合多组织转录组数据，找出多发性硬化症关键致病基因和通路。
Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data
- 构建端到端机器学习流程，融合单细胞与批量测序数据进行分类
- 在脑脊液B细胞中达AUC 0.94，显著优于传统分析方法
- 揭示非经典免疫检查点、病毒相关通路等新机制，适合疾病研究者参考
多发性硬化症（MS）是一种慢性自身免疫性中枢神经系统疾病，其分子机制尚未完全明确。本研究开发了一套端到端的机器学习流程，分析外周血单个核细胞和脑脊液中的转录组数据，整合了批量微阵列与单细胞RNA测序数据（重点关注CD4+和B细胞）。经过严格的预处理、批次校正和基因去聚类后，采用XGBoost分类器区分患者与健康对照。利用可解释人工智能工具SHAP识别驱动分类的关键基因，并与差异表达分析（DEA）结果对比。SHAP筛选基因进一步通过互作网络和通路富集分析深入研究。模型表现优异，尤其在脑脊液B细胞（AUC=0.94）和微阵列数据（AUC=0.86）中。SHAP基因选择与传统DEA互补。跨数据集识别出的基因簇揭示了免疫激活、非经典免疫检查点（ITK、CLEC2D、KLRG1、CEACAM1）、核糖体与翻译程序、泛素-蛋白酶体调控、脂质运输及爱泼斯坦-巴尔病毒相关通路。该整合且可解释的框架揭示了超越传统分析的互补见解，为MS发病机制提供了新的机制假说和潜在生物标志物。
原文摘要 · Abstract (English)
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In this study, we developed an end-to-end machine learning pipeline to analyze transcriptomic data from peripheral blood mononuclear cells and cerebrospinal fluid, integrating both bulk microarray and single-cell RNA sequencing datasets (concentrating on CD4+ and B-cells). After rigorous preprocessing, batch correction, and gene declustering, XGBoost classifiers were trained to distinguish MS patients from healthy controls. Explainable AI tools, namely SHapley Additive exPlanations (SHAP), were employed to identify key genes driving classification, and results were compared with Differential Expression Analysis (DEA). SHAP-prioritized genes were further investigated through interaction networks and pathway enrichment analyses. The models achieved strong performance, particularly in CSF B-cells (AUC=0.94) and microarray (AUC=0.86). SHAP gene selection proved to be complementary to classical DEA. Gene clusters identified across multiple datasets highlighted immune activation, non-canonical immune checkpoints (ITK, CLEC2D, KLRG1, CEACAM1), ribosomal and translational programs, ubiquitin-proteasome regulation, lipid trafficking, and Epstein-Barr virus-related pathways. Our integrative and explainable framework reveals complementary insights beyond conventional analysis and provides novel mechanistic hypotheses and potential biomarkers for MS pathogenesis.
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