Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data

TL;DR

This study presents an explainable machine learning pipeline integrating bulk and single-cell transcriptomics data to identify key genes and pathways involved in Multiple Sclerosis, offering new insights and potential biomarkers.

Contribution

The paper introduces an end-to-end machine learning framework combining explainable AI and multi-omics data for MS analysis, highlighting novel gene and pathway discoveries.

Findings

High classification accuracy in CSF B-cells (AUC=0.94) and microarray data (AUC=0.86)

SHAP gene selection complements differential expression analysis

Identification of immune activation, non-canonical checkpoints, and viral pathways

Abstract

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.